BIG-IP

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VMware vSphere to OpenShift Virtualization Migration with F5 BIG-IP
Guidance for your migration plans of BIG-IP from VMware vSphere to Red Hat OpenShift Virtualization
Ulises_Alonso
Mar 26, 2025 Place Technical Articles
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Introducing the F5 Application Study Tool (AST)
In the ever-evolving world of application delivery and security, gaining actionable insights into your infrastructure and applications has become more critical than ever. The Application Study Tool (AST) is designed to help technical teams and administrators leverage the power of open-source telemetry and visualization tools to enhance their monitoring, diagnostics, and analysis workflows.
Chad_Davis
Mar 18, 2025 Place Technical Articles
1.1KViews
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F5 BIG-IP deployment with Red Hat OpenShift - keeping client IP addresses and egress flows
Controlling the egress traffic in OpenShift allows to use the BIG-IP for several use cases: Keeping the source IP of the ingress clients Providing highly scalable SNAT for egress flows Providing security functionalities for egress flows
Ulises_Alonso
Mar 15, 2025 Place Technical Articles
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How to get a F5 BIG-IP VE Developer Lab License
(applies to BIG-IP TMOS Edition) To assist operational teams teams improve their development for the BIG-IP platform, F5 offers a low cost developer lab license. This license can be purchased from your authorized F5 vendor. If you do not have an F5 vendor, and you are in either Canada or the US you can purchase a lab license online: CDW BIG-IP Virtual Edition Lab License CDW Canada BIG-IP Virtual Edition Lab License Once completed, the order is sent to F5 for fulfillment and your license will be delivered shortly after via e-mail. F5 is investigating ways to improve this process. To download the BIG-IP Virtual Edition, log into my.f5.com (separate login from DevCentral), navigate down to the Downloads card under the Support Resources section of the page. Select BIG-IP from the product group family and then the current version of BIG-IP. You will be presented with a list of options, at the bottom, select the Virtual-Edition option that has the following descriptions: For VMware Fusion or Workstation or ESX/i: Image fileset for VMware ESX/i Server For Microsoft HyperV: Image fileset for Microsoft Hyper-V KVM RHEL/CentoOS: Image file set for KVM Red Hat Enterprise Linux/CentOS Note: There are also 1 Slot versions of the above images where a 2nd boot partition is not needed for in-place upgrades. These images include _1SLOT- to the image name instead of ALL. The below guides will help get you started with F5 BIG-IP Virtual Edition to develop for VMWare Fusion, AWS, Azure, VMware, or Microsoft Hyper-V. These guides follow standard practices for installing in production environments and performance recommendations change based on lower use/non-critical needs for development or lab environments. Similar to driving a tank, use your best judgement. Deploying F5 BIG-IP Virtual Edition on VMware Fusion Deploying F5 BIG-IP in Microsoft Azure for Developers Deploying F5 BIG-IP in AWS for Developers Deploying F5 BIG-IP in Windows Server Hyper-V for Developers Deploying F5 BIG-IP in VMware vCloud Director and ESX for Developers Note: F5 Support maintains authoritative Azure, AWS, Hyper-V, and ESX/vCloud installation documentation. VMware Fusion is not an official F5-supported hypervisor so DevCentral publishes the Fusion guide with the help of our Field Systems Engineering teams.
Chase_Abbott
Mar 13, 2025 Place Technical Articles
85KViews
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APM Configuration to Support Duo MFA using iRule
Overview BIG-IP APM has supported Duo as an MFA provider for a long time with RADIUS-based integration. Recently, Duo has added support for Universal Prompt that uses Open ID Connect (OIDC) protocol to provide two-factor authentication. To integrate APM as an OIDC client and resource server, and Duo as an Identity Provider (IdP), Duo requires the user’s logon name and custom parameters to be sent for Authentication and Token request. This guide describes the configuration required on APM to enable Duo MFA integration using an iRule. iRules addresses the custom parameter challenges by generating the needed custom values and saving them in session variables, which the OAuth Client agent then uses to perform MFA with Duo. This integration procedure is supported on BIG-IP versions 13.1, 14.1x, 15.1x, and 16.x. To integrate Duo MFA with APM, complete the following tasks: 1. Choose deployment type: Per-request or Per-session 2. Configure credentials and policies for MFA on the DUO web portal 3. Create OAuth objects on the BIG-IP system 4. Configure the iRule 5. Create the appropriate access policy/policies on the BIG-IP system 6. Apply policy/policies and iRule to the APM virtual server Choose deployment type APM supports two different types of policies for performing authentication functions. Per-session policies: Per-session policies provide authentication and authorization functions that occur only at the beginning of a user’s session. These policies are compatible with most APM use cases such as VPN, Webtop portal, Remote Desktop, federation IdP, etc. Per-request policies: Per-request policies provide dynamic authentication and authorization functionality that may occur at any time during a user’s session, such as step-up authentication or auditing functions only for certain resources. These policies are only compatible with Identity Aware Proxy and Web Access Management use cases and cannot be used with VPN or webtop portals. This guide contains information about setting up both policy types. Prerequisites Ensure the BIG-IP system has DNS and internet connectivity to contact Duo directly for validating the user's OAuth tokens. Configure credentials and policies for MFA on Duo web portal Before you can protect your F5 BIG-IP APM Web application with Duo, you will first need to sign up for a Duo account. 1. Log in to the Duo Admin Panel and navigate to Applications. 2. Click Protect an application. Figure 1: Duo Admin Panel – Protect an Application 3. Locate the entry for F5 BIG-IP APM Web in the applications list and click Protect to get the Client ID, Client secret, and API hostname. You will need this information to configure objects on APM. Figure 2: Duo Admin Panel – F5 BIG-IP APM Web 4. As DUO is used as a secondary authentication factor, the user’s logon name is sent along with the authentication request. Depending on your security policy, you may want to pre-provision users in Duo, or you may allow them to self-provision to set their preferred authentication type when they first log on. To add users to the Duo system, navigate to the Dashboard page and click the Add New... -> Add User button. A Duo username should match the user's primary authentication username. Refer to the https://duo.com/docs/enrolling-users link for the different methods of user enrollment. Refer to Duo Universal Prompt for additional information on Duo’s two-factor authentication. Create OAuth objects on the BIG-IP system Create a JSON web key When APM is configured to act as an OAuth client or resource server, it uses JSON web keys (JWKs) to validate the JSON web tokens it receives from Duo. To create a JSON web key: 1. On the Main tab, select Access > Federation > JSON Web Token > Key Configuration. The Key Configuration screen opens. 2. To add a new key configuration, click Create. 3. In the ID and Shared Secret fields, enter the Client ID and Client Secret values respectively obtained from Duo when protecting the application. 4. In the Type list, select the cryptographic algorithm used to sign the JSON web key. Figure 3: Key Configuration screen 5. Click Save. Create a JSON web token As an OAuth client or resource server, APM validates the JSON web tokens (JWT) it receives from Duo. To create a JSON web token: 1. On the Main tab, select Access > Federation > JSON Web Token > Token Configuration. The Token Configuration screen opens. 2. To add a new token configuration, click Create. 3. In the Issuer field, enter the API hostname value obtained from Duo when protecting the application. 4. In the Signing Algorithms area, select from the Available list and populate the Allowed and Blocked lists. 5. In the Keys (JWK) area, select the previously configured JSON web key in the allowed list of keys. Figure 4: Token Configuration screen 6. Click Save. Configure Duo as an OAuth provider APM uses the OAuth provider settings to get URIs on the external OAuth authorization server for JWT web tokens. To configure an OAuth provider: 1. On the Main tab, select Access > Federation > OAuth Client / Resource Server > Provider. The Provider screen opens. 2. To add a provider, click Create. 3. In the Name field, type a name for the provider. 4. From the Type list, select Custom. 5. For Token Configuration (JWT), select a configuration from the list. 6. In the Authentication URI field, type the URI on the provider where APM should redirect the user for authentication. The hostname is the same as the API hostname in the Duo application. 7. In the Token URI field, type the URI on the provider where APM can get a token. The hostname is the same as the API hostname in the Duo application. Figure 5: OAuth Provider screen 8. Click Finished. Configure Duo server for APM The OAuth Server settings specify the OAuth provider and role that Access Policy Manager (APM) plays with that provider. It also sets the Client ID, Client Secret, and Client’s SSL certificates that APM uses to communicate with the provider. To configure a Duo server: 1. On the Main tab, select Access > Federation > OAuth Client / Resource Server > OAuth Server. The OAuth Server screen opens. 2. To add a server, click Create. 3. In the Name field, type a name for the Duo server. 4. From the Mode list, select how you want the APM to be configured. 5. From the Type list, select Custom. 6. From the OAuth Provider list, select the Duo provider. 7. From the DNS Resolver list, select a DNS resolver (or click the plus (+) icon, create a DNS resolver, and then select it). 8. In the Token Validation Interval field, type a number. In a per-request policy subroutine configured to validate the token, the subroutine repeats at this interval or the expiry time of the access token, whichever is shorter. 9. In the Client Settings area, paste the Client ID and Client secret you obtained from Duo when protecting the application. 10. From the Client's ServerSSL Profile Name, select a server SSL profile. Figure 6: OAuth Server screen 11. Click Finished. Configure an auth-redirect-request and a token-request Requests specify the HTTP method, parameters, and headers to use for the specific type of request. An auth-redirect-request tells Duo where to redirect the end-user, and a token-request accesses the authorization server for obtaining an access token. To configure an auth-redirect-request: 1. On the Main tab, select Access > Federation > OAuth Client / Resource Server > Request. The Request screen opens. 2. To add a request, click Create. 3. In the Name field, type a name for the request. 4. For the HTTP Method, select GET. 5. For the Type, select auth-redirect-request. 6. As shown in Figure 7, specify the list of GET parameters to be sent: request parameter with value depending on the type of policy For per-request policy: %{subsession.custom.jwt_duo} For per-session policy: %{session.custom.jwt_duo} client_id parameter with type client-id response_type parameter with type response-type Figure 7: Request screen with auth-redirect-request (Use “subsession.custom…” for Per-request or “session.custom…” for Per-session) 7. Click Finished. To configure a token-request: 1. On the Main tab, select Access > Federation > OAuth Client / Resource Server > Request. The Request screen opens. 2. To add a request, click Create. 3. In the Name field, type a name for the request. 4. For the HTTP Method, select POST. 5. For the Type, select token-request. 6. As shown in Figure 8, specify the list of POST parameters to be sent: client_assertion parameter with value depending on the type of policy For per-request policy: %{subsession.custom.jwt_duo_token} For per-session policy: %{session.custom.jwt_duo_token} client_assertion_type parameter with value urn:ietf:params:oauth:client-assertion-type:jwt-bearer grant_type parameter with type grant-type redirect_uri parameter with type redirect-uri Figure 8: Request screen with token-request (Use “subsession.custom…” for Per-request or “session.custom…” for Per-session) 7. Click Finished. Configure the iRule iRules gives you the ability to customize and manage your network traffic. Configure an iRule that creates the required sub-session variables and usernames for Duo integration. Note: This iRule has sections for both per-request and per-session policies and can be used for either type of deployment. To configure an iRule: 1. On the Main tab, click Local Traffic > iRules. 2. To create an iRules, click Create. 3. In the Name field, type a name for the iRule. 4. Copy the sample code given below and paste it in the Definition field. Replace the following variables with values specific to the Duo application: <Duo Client ID> in the getClientId function with Duo Application ID. <Duo API Hostname> in the createJwtToken function with API Hostname. For example, https://api-duohostname.com/oauth/v1/token. <JSON Web Key> in the getJwkName function with the configured JSON web key. Note: The iRule ID here is set as JWT_CREATE. You can rename the ID as desired. You specify this ID in the iRule Event agent in Visual Policy Editor. when ACCESS_POLICY_AGENT_EVENT { if { [ACCESS::policy agent_id] eq "JWT_CREATE" } { set duo_uname [ACCESS::session data get "session.logon.last.username"] # Inline logic for creating JWT set header "{\"alg\":\"HS512\",\"typ\":\"JWT\"}" set exp [expr {[clock seconds] + 900}] set client_id "<Duo Client ID>" set redirect_uri "https://[ACCESS::session data get session.server.network.name]/oauth/client/redirect" set payload "{\"response_type\": \"code\",\"scope\":\"openid\",\"exp\":${exp},\"client_id\":\"${client_id}\",\"redirect_uri\":\"${redirect_uri}\",\"duo_uname\":\"${duo_uname}\"}" set jwt_duo [ACCESS::oauth sign -header $header -payload $payload -alg HS512 -key "<JSON Web Key>"] ACCESS::session data set session.custom.jwt_duo $jwt_duo # JWT Token creation set aud "<Duo API Hostname>" set jti [string range [clock seconds] 0 31] set token_payload "{\"sub\": \"${client_id}\",\"iss\":\"${client_id}\",\"aud\":\"${aud}\",\"exp\":${exp},\"jti\":\"${jti}\"}" set jwt_duo_token [ACCESS::oauth sign -header $header -payload $token_payload -alg HS512 -key "<JSON Web Key>"] ACCESS::session data set session.custom.jwt_duo_token $jwt_duo_token } } when ACCESS_PER_REQUEST_AGENT_EVENT { if { [ACCESS::perflow get perflow.irule_agent_id] eq "JWT_CREATE" } { set duo_uname [ACCESS::session data get "session.logon.last.username"] set header "{\"alg\":\"HS512\",\"typ\":\"JWT\"}" set exp [expr {[clock seconds] + 900}] set client_id "<Duo Client ID>" set redirect_uri "https://[ACCESS::session data get session.server.network.name]/oauth/client/redirect" set payload "{\"response_type\": \"code\",\"scope\":\"openid\",\"exp\":${exp},\"client_id\":\"${client_id}\",\"redirect_uri\":\"${redirect_uri}\",\"duo_uname\":\"${duo_uname}\"}" set jwt_duo [ACCESS::oauth sign -header $header -payload $payload -alg HS512 -key "<JSON Web Key>"] ACCESS::perflow set perflow.custom $jwt_duo # JWT Token creation set aud "<Duo API Hostname>" set jti [string range [clock seconds] 0 31] set token_payload "{\"sub\": \"${client_id}\",\"iss\":\"${client_id}\",\"aud\":\"${aud}\",\"exp\":${exp},\"jti\":\"${jti}\"}" set jwt_duo_token [ACCESS::oauth sign -header $header -payload $token_payload -alg HS512 -key "<JSON Web Key>"] ACCESS::perflow set perflow.scratchpad $jwt_duo_token } } Note: iRule updated 11/27/2024 to eliminate CMP demotion. Figure 9: iRule screen 5. Click Finished. Create the appropriate access policy/policies on the BIG-IP system Per-request policy Skip this section for a per-session type deployment The per-request policy is used to perform secondary authentication with Duo. Configure the access policies through the access menu, using the Visual Policy Editor. The per-request access policy must have a subroutine with an iRule Event, Variable Assign, and an OAuth Client agent that requests authorization and tokens from an OAuth server. You may use other per-request policy items such as URL branching or Client Type to call Duo only for certain target URIs. Figure 10 shows a subroutine named duosubroutine in the per-request policy that handles Duo MFA authentication. Figure 10: Per-request policy in Visual Policy Editor Configuring the iRule Event agent The iRule Event agent specifies the iRule ID to be executed for Duo integration. In the ID field, type the iRule ID as configured in the iRule. Figure 11: iRule Event agent in Visual Policy Editor Configuring the Variable Assign agent The Variable Assign agent specifies the variables for token and redirect requests and assigns a value for Duo MFA in a subroutine. This is required only for per-request type deployment. Add sub-session variables as custom variables and assign their custom Tcl expressions as shown in Figure 12. subsession.custom.jwt_duo_token = return [mcget {perflow.scratchpad}] subsession.custom.jwt_duo = return [mcget {perflow.custom}] Figure 12: Variable Assign agent in Visual Policy Editor Configuring the OAuth Client agent An OAuth Client agent requests authorization and tokens from the Duo server. Specify OAuth parameters as shown in Figure 13. In the Server list, select the Duo server to which the OAuth client directs requests. In the Authentication Redirect Request list, select the auth-redirect-request configured earlier. In the Token Request list, select the token-request configured earlier. Some deployments may not need the additional information provided by OpenID Connect. You could, in that case, disable it. Figure 13: OAuth Client agent in Visual Policy Editor Per-session policy Configure the Per Session policy as appropriate for your chosen deployment type. Per-request: The per-session policy must contain at least one logon page to set the username variable in the user’s session. Preferably it should also perform some type of primary authentication. This validated username is used later in the per-request policy. Per-session: The per-session policy is used for all authentication. A per-request policy is not used. Figures 14a and 14b show a per-session policy that runs when a client initiates a session. Depending on the actions you include in the access policy, it can authenticate the user and perform actions that populate session variables with data for use throughout the session. Figure 14a: Per-session policy in Visual Policy Editor performs both primary authentication and Duo authentication (for per-session use case) Figure 14b: Per-session policy in Visual Policy Editor performs primary authentication only (for per-request use case) Apply policy/policies and iRule to the APM virtual server Finally, apply the per-request policy, per-session policy, and iRule to the APM virtual server. You assign iRules as a resource to the virtual server that users connect. Configure the virtual server’s default pool to the protected local web resource. Apply policy/policies to the virtual server Per-request policy To attach policies to the virtual server: 1. On the Main tab, click Local Traffic > Virtual Servers. 2. Select the Virtual Server. 3. In the Access Policy section, select the policy you created. 4. Click Finished. Figure 15: Access Policy section in Virtual Server (per-request policy) Per-session policy Figure 16 shows the Access Policy section in Virtual Server when the per-session policy is deployed. Figure 16: Access Policy section in Virtual Server (per-session policy) Apply iRule to the virtual server To attach the iRule to the virtual server: 1. On the Main tab, click Local Traffic > Virtual Servers. 2. Select the Virtual Server. 3. Select the Resources tab. 4. Click Manage in the iRules section. 5. Select an iRule from the Available list and add it to the Enabled list. 6. Click Finished.
Hardeep_Kaur
Mar 03, 2025 Place Technical Articles
18KViews
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F5 Distributed Cloud Security Service Insertion With BIG-IP Advanced WAF
In this article we will show you how to quickly deploy and operate external services of your choice across multiple public clouds. For this article I will select the BIG-IP Advanced WAF (PAYG), future articles will cover additional solutions. Co-Author: Anitha Mareedu, Sr. Security Engineer, F5 Introduction F5’s Distributed Cloud Securtiy Service Insertion solution allows enterprises to deploy and operate external services of their choice across multiple public clouds. Let's start by looking at a real-world customer example. The enterprise has standardized on an external firewall in their private data center. Their network and security team are very familiar with using BIG-IP AWAF. They want to deploy the same security firewall solution that they use in the private datacenter in the public cloud. The requirements are: a simple operational model to deploy these services a unified security policy consistency across different clouds simple deployments unified logging Challenges Customers have identified several challenges in moving to the cloud. Initallly, teams that are very familiar with supporting services in their private data center usually do not have the expertise in designing, deploying and supporting in public clouds. If the same team then is tasked with deploying to multiple clouds the gap widens, terminology, archtitecture tools and constructs are all unique. Second, the operational models are different across different clouds. In AWS, you use either a VPC or a transit gateway (TGW), in Azure you use a VNET and Google has VPC’s. Solution Description Let's look at how F5’s Distributed Cloud Security Service insertion solution helps simplify and unify security solution deployments in multi-cloud and hybrid cloud environments: Infrastructure-as-code: Implementation and policy configuration can be automated and run as infrastructure-as-code across clouds and regions, allowing policies to be repeatable in any major public or private cloud. Easy setup and management: This simplified setup and management extends across AWS, Azure, and other clouds, as the F5 Distributed Cloud Platform supports AWS Transit Gateway, virtual network peering in Azure, and use of VPC attachments. Define once and replicate models: No extra handcrafting is needed for consistent, straightforward operations and deployment. Unified traffic steering rules: With the Distributed Cloud Platform, traffic is rerouted from networks through the security service using the same steering rules across different public and private clouds. Using F5 Distributed Cloud Console, IT pros get granular visibility and single-pane-of-glass management of traffic across clouds and networks. Optional policy deployment routes: Policies can be deployed at either or both the network layer (using IP addresses) or the application layer (using APIs). Diagram Step by Step Process This walk thru assumes you already have an AWS VPC deployed. Have handy the VPC id. Log into the F5 Distributed Cloud Dashboard You are presented with the Dashboard where you can choose which deployment option you want to work with. We will be working with Cloud and Edge Sites. Select Cloud and Edge Sites > Manage > Site Management > AWS TWG Sites Click Add the AWS Transit Gateway (TWG) Under Metadata give your TWG site a Name, Label and Description Click on Configure under AWS Configuration This brings up the Services VPC Configuration Page Select your AWS region Select Services VPC, leave as New, let it genetrate a name or choose your own name and give the Primary CIDR block you want to assign to the VPC. Leave Transit Gateway as New TWG Leave BGP as Automatic Under Site Node Parameters, Ingress/ Egress select “Add Item” Move slider on upper right corner to Show Advanced Fields Fill in required configuration, AWS AZ Name and CIDR Blocks for each of the the subnets and click the “Add Item” You can let the system autogenerate these or assign the desired range. This will take you back to the last screen, where you need to either create or select your cloud credentials. These are Programmatic Access Credentials allowing API access. Click Apply This takes you to the previous screen where we connect your current VPC to the Service VPC we are creating. (have VPC id available) Click Configure under VPC attachments Click Add Item Supply VPC id Click Apply This takes you back once again to the AWS TWG Site Screen. Finish with clicking Save and Exit. In the UI you will then click Apply. You are now deploying your new Security VPC via Terraform. While that is deploying we will move on to the External Services. Manage > Site Management > External Services > Add External Service Give your Service a name, add a label and description. Click “Configure” under Select NFV Service Provider. For this article we will select the F5 BIG-IP Advanced WAF (PAYG), future articles will cover additional solutions. Provide the Admin Password Admin Username public SSH Key that you will use to access your BIG-IP deployment. Select the TWG site you created above. Finally click “Add Item“ under Service Nodes. Service nodes Enter a Node name and the Avilibilty Zones you wish to delpoy into. Then click “Add Item” This will take you back to the original screen. Enable HTTPS Management of Nodes, supply a delegated doman that will issue a Certificate. Under Select Service Type” Keep Inside VIP at Automatic and Set the Outside VIP to “Advertise On Outside Network”. Finally Click “Save and Exit” At the end, the External Security Service is deployed, and you are taken to all the External Services. Click the name of the External Service you deployed to expand the details From this screen you are able to access several items, the two I want to point out are the TGW stats and the BIG-IP you deployed by clicking the Management Dashboard URL. Click under Site the TWG Service you deployed Here you are able to see fine grained stats under all the tabs. System Metrics Application Metrics Site Status Nodes Interfaces Alerts Requests Top Talkers Connections TWG Flow tables DHCP Status Objects Tools Going back click the hyperlink to the BIG-IP if you wish to look at the configuration. F5 Distributed Cloud Service Insertion automatically configured your BIG-IP with the following information: • Interfaces • Self IPs • Routes • Management and credentials • VLANs • IPoIP tunnel SI<-> BIG-IP • VIP The following two items will need to be configured on your BIG-IP. This configuration Configure AWAF policies SecOps can access familiar BIG-IP UI using management link provided in F5 Cloud Console and set up and configure AWAF ploicies Define a Traffic Steering Policy Network traffic to define traffic steering policy at Network (L3/L4) layer Service policy to define traffic steering policy at App(L7) level. Below are the traffic steering control methods available: Network level – Ip address, port, etc App level – API, Method, etc At the end of this step, you can see traffic getting diverted to BIG-IP and getting inspected by BIG-IP. Summary As you can see, F5 Distributed Cloud Security Service Insertion dramatically reduces the operation complexity for deploying external services in public clouds, it greatly enhances the security posture and it vastly improves productivity for all the operations teams such as NetOps, SecOps or DevOps.
Ted_Byerly
Mar 03, 2025 Place Technical Articles
2.1KViews
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Mastering Imperative and Declarative Automation with F5 BIG-IP – AppWorld 2025
At AppWorld 2025, Anton Varkalevich and I will be hosting a hands-on lab, "Mastering Imperative and Declarative Automation with F5 BIG-IP." I'm Matt Mabis, a Senior Solutions Architect in the Business Development/Technical Alliances department at F5. I specialize in solution integrations with our partnerships with Red Hat (Ansible and OpenShift), VMware, and Omnissa Horizon. Anton Varkalevich is a Solutions Engineer III for Financial Services at F5, specializing in ensuring financial customers have the right solutions to meet their unique needs. Automation plays a critical role in financial institutions, enabling them to serve their customers quickly and effectively. This lab is the result of years of experience working with customers to streamline application deployments on F5 BIG-IP. We’ll use Ansible Automation Platform to demonstrate both imperative and declarative automation approaches. Participants will first deploy common use cases—such as HTTPS and SSL-delivered applications—using imperative automation with individual Ansible modules. Then, we’ll achieve the same outcomes using declarative automation, offering a side-by-side comparison to help attendees choose the best approach for their needs. By the end of this lab, you’ll have a solid understanding of both automation styles and how to apply them effectively in your environment. Join us at AppWorld 2025—we look forward to sharing our knowledge with you!
Matt_Mabis
Feb 20, 2025 Place Technical Articles
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External Monitor Information and Templates
This article is written by, and published on behalf of, DevCentral MVP Leonardo Souza. --- Introduction External monitors have been in use on F5 devices for a long time, and you can find a lot of code and articles about them here on DevCentral. However, most of those articles and code samples are very old and outdated. So, I think is a good time to update that with new information and new templates. In this article, I will provide some information about external monitors and templates you can use to build your own external monitor script and how you setup an external monitor. External Monitor The external monitor is a script that the F5 device will run to determine the status of a pool member (note: you can’t assign an external monitor to a node). The script runs the code and indicates if it was successful or not. The script indicates success outputting something to the standard output, and failure if nothing was outputted. The external monitor is your last resource in relation to monitoring, only to be used if there is no built-in monitoring that could be used to perform the monitoring. Built-in monitors are generally faster and easier to support as they do not require programming skills. External monitors run on Linux, while some built-in monitors can run on Linux or TMM. Linux is the kernel that CentOS uses, and CenOS is the base system that F5 uses, also known as the host system. TMM is the Kernel that F5 uses on TMOS, which is the F5 system. Running a built-in monitor in TMM is faster than in Linux. TMM monitors were introduced in version 13.1.0, for more information read this solution: https://support.f5.com/csp/article/K11323537 Configuration Steps Note: Instructions are for version 15.1.0, but should be similar in other versions, and you only need to setup this configuration in one device if you have an HA pair, as the configuration is shared during the config sync. Let’s create a simple external monitor to ping a pool member. You can do via tmsh, but it is simpler via GUI. Download the following file: https://raw.githubusercontent.com/leonardobdes/External_Monitor_Templates/master/bash_external_monitor_template.sh In the GUI go to System > File Management > External Monitor Program File List > Click Import. Select the file and give a name “simple_script” to the script, and import. That will import the file to the system, and make it available in the GUI. Now create a new monitor that points to the script file. In the GUI go to Local Traffic > Monitor > Click Create. Give the name “simple_monitor”, and select type as External. In the External Program select simple_script, click Finished. Now apply the new monitor to a pool member or pool. Here is the full configuration created: sys file external-monitor simple_script { checksum SHA1:817:01e135449c710e21dd090fdd688d940e319f97f5 create-time 2020-04-23:17:45:25 created-by admin last-update-time 2020-04-23:17:45:25 mode 33261 revision 1 size 817 source-path none updated-by admin } ltm monitor external simple_monitor { defaults-from external interval 5 run /Common/simple_script time-until-up 0 timeout 16 } ltm pool pool_asm { members { LABServerPHP:http { address 172.16.0.14 session monitor-enabled state up } test_server:http { address 172.16.0.19 session monitor-enabled state down } } monitor simple_monitor } The first part is the script imported to the system. Next, the monitor that was created. Lastly, a pool using that monitor, also showing that one pool member is up and another one is down. Templates You can write the script to be used in the external monitor in any language that is supported by the F5 devices. I created a template for each of the following languages: Bash Perl Python Tcl For the templates go to this codeshare link: External Monitor Templates | DevCentral On my speed tests, I got these times with the templates: Bash - 0.014 seconds Perl - 0.020 seconds Tcl - 0.021 seconds Python - 0.084 seconds As you can see, Bash is definitely the fastest. Perl and Tcl are very close, and Python is the slowest. But, you may need to use something that works very well in Python, so it might be your only option. Bash Template I will explain the Bash template, but the logic is the same for other programming languages. Each script has 2 versions, one with debug and one without, as indicated in their filename. I will explain the one with debug. This tells the system what to use to run the script. #!/bin/bash The script will be called with 2 arguments, first is the IP, and second the port. The IP is passed in IPv6 format, example “::ffff:172.16.0.14”, so you need to remove “::ffff:” Old scripts, and also the sample_monitor script that comes in the system, use “sed 's/::ffff://'`” that are very slow compared with the above. ip=${1:7} port=$2 This will create a log line in the file /var/log/ltm. logger -p local0.notice "$MON_TMPL_NAME - PID: $$ Name: $NODE_NAME IP: $ip Port: $port" This is where you add your code to test the pool member. This example is just a ping against the IP, and the “-c1” means a single packet. The script then saves the result to know if it failed or not. Next, it will log another line to /var/log/ltm. ping -c1 $ip &> /dev/null result=$? logger -p local0.notice "$MON_TMPL_NAME - PID: $$ Result: $result" Lastly, the scripts check if the result was successful. If successful, it will send up to the standard output. [ $result -eq 0 ] && echo "up" The log lines will be similar to these ones. Apr 24 13:59:15 LABBIGIP1.lab.local notice logger[5178]: /Common/simple_monitor - PID: 5177 Name: /Common/test_server IP: 172.16.0.19 Port: 80 Apr 24 13:59:18 LABBIGIP1.lab.local notice logger[5201]: /Common/simple_monitor - PID: 5200 Name: /Common/LABServerPHP IP: 172.16.0.14 Port: 80 Apr 24 13:59:18 LABBIGIP1.lab.local notice logger[5203]: /Common/simple_monitor - PID: 5200 Result: 0 Apr 24 13:59:18 LABBIGIP1.lab.local notice logger[5209]: /Common/simple_monitor - PID: 5177 Result: 1 Note that the monitoring tests can run in parallel, so the lines will not be sequential. Use the PID number (Process ID), to match the lines. Old scripts, and also the sample_monitor script that comes in the system, have multiple lines of code to check if the same monitor is still running. If running, the new process kills the old one, before performing the monitoring. I tested this behavior in version 11.3.0 and 15.1.0, and this is not necessary. Before starting the new process for the script, the system will kill the old one, so any code in the script to control that is unnecessary as it will not be used. I assume this was necessary for old versions, as I never tested, but is not necessary for currently supported versions. Arguments and Variables The external monitor has multiple settings, including the ones you will find in any other monitor, like interval and timeout. However, it has 2 extra settings, those are arguments and variables. Arguments are passed when calling the script, for example: bash simple_script.sh IP port argument3 Variables are set as environment variables. You can then access that variable in the script. Be aware that the system will add the variables from the monitor first, and then add some extra variables. The environment variables for version 11.3.0 are, with values as an example: MON_TMPL_NAME=/Common/bash_external_monitor_template ARGS_I=2 3 abc X=b PATH=/sbin:/bin:/usr/sbin:/usr/bin:/usr/contrib/bin:/usr/local/bin:/usr/contrib/sbin:/usr/local/sbin:/usr/libexec NODE_PORT=80 PWD=/var/run SHLVL=1 NODE_IP=::ffff:172.16.0.19 NODE_NAME=/Common/test_server RUN_I=/Common/bash_external_monitor_template _=/bin/env ARGS_I will include the arguments you set in the monitor configuration, in this case, I have “2 3 abc”. X is the variable I added in the monitor configuration, with value “b”. That means you can use those variables in your script. Also, if you set the variable $PATH in your script as an example, it will get reset by the system, as the system variables are set after the monitor configuration variables. Let’s expand that with examples. Here is a monitor configuration with one argument and one variable. ltm monitor external simple_monitor { args 2 defaults-from external interval 5 run /Common/simple_script time-until-up 0 timeout 16 user-defined debug 1 } Here is the part of the script that changed: ping -c${3} $ip &> /dev/null result=$? [[ $debug -eq 1 ]] && logger -p local0.notice "$MON_TMPL_NAME - PID: $$ Result: $result Ping: ${3}" Now the script is using the argument 3 for the ping command, and the variable debug to define if log should occur. Let’s see the logs created: Apr 24 12:18:04 LABBIGIP1.lab.local notice logger[8179]: /Common/simple_monitor - PID: 8178 Name: /Common/test_server IP: 172.16.0.19 Port: 80 Apr 24 12:18:06 LABBIGIP1.lab.local notice logger[8196]: /Common/simple_monitor - PID: 8195 Name: /Common/LABServerPHP IP: 172.16.0.14 Port: 80 Apr 24 12:18:07 LABBIGIP1.lab.local notice logger[8206]: /Common/simple_monitor - PID: 8178 Result: 1 Ping: 2 Apr 24 12:18:07 LABBIGIP1.lab.local notice logger[8208]: /Common/simple_monitor - PID: 8195 Result: 0 Ping: 2 Ping is done with 2 packets, and the second log line still logged. Testing the Script External scripts are saved in this location: /config/filestore/files_d/Common_d/external_monitor_d That is for the Common partition, replace Common to the name of the partition if you import the script in another partition. The file will have an ID number (100479), and also a version number that changes when you update the file (19): :Common:simple_script_100479_19 That means you can go to that directory and run the script from there, and troubleshoot the errors. Don’t forget that when testing this way, you will not have the environment variables the system setup, you will have the environment variables from the shell where you run the command. Let’s test the simple simple_monitor discussed above. First, create the environment variables: export debug=1 MON_TMPL_NAME='/Common/simple_monitor' NODE_NAME='/Common/test_server' Run the script: ./\:Common\:simple_script_100479_22 '::ffff:172.16.0.14' 80 2 You will get the following log lines: Apr 24 16:03:48 LABBIGIP1.lab.local notice root[12206]: /Common/simple_monitor - PID: 12205 Name: /Common/test_server IP: 172.16.0.14 Port: 80 Apr 24 16:03:49 LABBIGIP1.lab.local notice root[12224]: /Common/simple_monitor - PID: 12205 Result: 0 Ping: 2 You need to escape the “:” with \. After you do the tests, delete the environment variables created: unset debug MON_TMPL_NAME NODE_NAME Conclusion I hope this article provides you all the information you need to create your own external monitor. After you understand how it works, it is very simple to use. Just don’t forget, no “echo down”, only output something when the monitor was successful, and make sure you suppress any command output.
LiefZimmerman
Jan 27, 2025 Place Technical Articles
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F5 BIG-IP and NetApp StorageGRID - Providing Fast and Scalable S3 API for AI apps
F5 BIG-IP, an industry-leading ADC solution, can provide load balancing services for HTTPS servers, with full security applied in-flight and performance levels to meet any enterprise’s capacity targets. Specific to S3 API, the object storage and retrieval protocol that rides upon HTTPS, an aligned partnering solution exists from NetApp, which allows a large-scale set of S3 API targets to ingest and provide objects. Automatic backend synchronization allows any node to be offered up at a target by a server load balancer like BIG-IP. This allows overall storage node utilization to be optimized across the node set, and scaled performance to reach the highest S3 API bandwidth levels, all while offering high availability to S3 API consumers. S3 compatible storage is becoming popular for AI applications due to its superior performance over traditional protocols such as NFS or CIFS, as well as enabling repatriation of data from the cloud to on-prem. These are scenarios where the amount of data faced is large, this drives the requirement for new levels of scalability and performance; S3 compatible object storages such as NetApp StorageGRID are purpose-built to reach such levels. Sample BIG-IP and StorageGRID Configuration This document is based upon tests and measurements using the following lab configuration. All devices in the lab were virtual machine-based offerings. The S3 service to be projected to the outside world, depicted in the above diagram and delivered to the client via the external network, will use a BIG-IP virtual server (VS) which is tied to an origin pool of three large-capacity StorageGRID nodes. The BIG-IP maintains the integrity of the NetApp nodes by frequent HTTP-based health checks. Should an unhealthy node be detected, it will be dropped from the list of active pool members. When content is written via the S3 protocol to any node in the pool, the other members are synchronized to serve up content should they be selected by BIG-IP for future read requests. The key recommendations and observations in building the lab include: Setup a local certificate authority such that all nodes can be trusted by the BIG-IP. Typically the local CA-signed certificate will incorporate every node’s FQDN and IP address within the listed subject alternate names (SAN) to make the backend solution streamlined with one single certificate. Different F5 profiles, such as FastL4 or FastHTTP, can be selected to reach the right tradeoff between the absolute capacity of stateful traffic load-balanced versus rich layer 7 functions like iRules or authentication. Modern techniques such as multi-part uploads or using HTTP Ranges for downloads can take large objects, and concurrently move smaller pieces across the load balancer, lowering total transaction times, and spreading work over more CPU cores. The S3 protocol, at its core, is a set of REST API calls. To facilitate testing, the widely used S3Browser (www.s3browser.com) was used to quickly and intuitively create S3 buckets on the NetApp offering and send/retrieve objects (files) through the BIG-IP load balancer. Setup the BIG-IP and StorageGrid Systems The StorageGrid solution is an array of storage nodes, provisioned with the help of an administrative host, the “Grid Manager”. For interactive users, no thick client is required as on-board web services allow a streamlined experience all through an Internet browser. The following is an example of Grid Manager, taken from a Chrome browser; one sees the three Storage Nodes setup have been successfully added. The load balancer, in our case, the BIG-IP, is set up with a virtual server to support HTTPS traffic and distributed that traffic, which is S3 object storage traffic, to the three StorageGRID nodes. The following screenshot demonstrates that the BIG-IP is setup in a standard HA (active-passive pair) configuration and the three pool members are healthy (green, health checks are fine) and receiving/sending S3 traffic, as the byte counts are seen in the image to be non-zero. On the internal side of the BIG-IP, TCP port 18082 is being used for S3 traffic. To do testing of the solution, including features such as multi-part uploads and downloads, a popular S3 tool, S3Browser, was downloaded and used. The following shows the entirety of the S3Browser setup. Simply create an account (StorageGRID-Account-01 in our example) and point the REST API endpoint at the BIG-IP Virtual Server that is acting as the secure front door for our pool of NetApp nodes. The S3 Access Key ID and Secret values are generated at turn-up time of the NetApp appliances. All S3 traffic will, of course, be SSL/TLS encrypted. BIG-IP will intercept the SSL traffic (high-speed decrypt) and then re-encrypt when proxying the traffic to a selected origin pool member. Other valid load balancer setups exist; one might include an “off load” approach to SSL, whereby the S3 nodes safely co-located in a data center may prefer to receive non-SSL HTTP S3 traffic. This may see an overall performance improvement in terms of peak bandwidth per storage node, but this comes at the tradeoff of security considerations. Experimenting with S3 Protocol and Load Balancing With all the elements in place to start understanding the behavior of S3 and spreading traffic across NetApp nodes, a quick test involved creating a S3 bucket and placing some objects in that new bucket. Buckets are logical collections of objects, conceptually not that different from folders or directories in file systems. In fact, a S3 bucket could even be mounted as a folder in an operating system such as Linux. In their simplest form, most commonly, buckets can simply serve as high-capacity, performant storage and retrieval targets for similarly themed structured or unstructured data. In the first test, we created a new bucket (“audio-clip-bucket”) and uploaded four sample files to the new bucket using S3Browser. We then zeroed the statistics for each pool member on the BIG-IP, to see if even this small upload would spread S3 traffic across more than a single NetApp device. Immediately after the upload, the counters reflect that two StorageGRID nodes were selected to receive S3 transactions. Richly detailed, per-transaction visibility can be obtained by leveraging the F5 SSL Orchestrator (SSLO) feature on the BIG-IP, whereby copies of the bi-directional S3 traffic decrypted within the load balancer can be sent to packet loggers, analytics tools, or even protocol analyzers like Wireshark. The BIG-IP also has an onboard analytics tool, Application Visibility and Reporting (AVR) which can provide some details on the nuances of the S3 traffic being proxied. AVR demonstrates the following characteristics of the above traffic, a simple bucket creation and upload of 4 objects. With AVR, one can see the URL values used by S3, which include the bucket name itself as well as transactions incorporating the object names as URLs. Also, the HTTP methods used included both GETS and PUTS. The use of HTTP PUT is expected when creating a new bucket. S3 is not governed by a typical standards body document, such as an IETF Request for Comment (RFC), but rather has evolved out of AWS and their use of S3 since 2006. For details around S3 API characteristics and nomenclature, this site can be referenced. For example, the expected syntax for creating a bucket is provided, including the fact that it should be an HTTP PUT to the root (/) URL target, with the bucket configuration parameters including name provided within the HTTP transaction body. Achieving High Performance S3 with BIG-IP and StorageGRID A common concern with protocols, such as HTTP, is head-of-line blocking, where one large, lengthy transaction blocks subsequent desired, queued transactions. This is one of the reasons for parallelism in HTTP, where loading 30 or more objects to paint a web page will often utilize two, four, or even more concurrent TCP sessions. Another performance issue when dealing with very large transactions is, without parallelism, even those most performant networks will see an established TCP session reach a maximum congestion window (CWND) where no more segments may be in flight until new TCP ACKs arrive back. Advanced TCP options like TCP exponential windowing or TCP SACK can help, but regardless of this, the achievable bandwidth of any one TCP session is bounded and may also frequently task only one core in multi-core CPUs. With the BIG-IP serving as the intermediary, large S3 transactions may default to “multi-part” uploads and downloads. The larger objects become a series of smaller objects that conveniently can be load-balanced by BIG-IP across the entire cluster of NetApp nodes. As displayed in the following diagram, we are asking for multi-part uploads to kick in for objects larger than 5 megabytes. After uploading a 20-megabyte file (technically, 20,000,000 bytes) the BIG-IP shows the traffic distributed across multiple NetApp nodes to the tune of 160.9 million bits. The incoming bits, incoming from the perspective of the origin pool members, confirm the delivery of the object with a small amount of protocol overhead (bits divided by eight to reach bytes). The value of load balancing manageable chunks of very large objects will pay dividends over time with faster overall transaction completion times due to the spreading of traffic across NetApp nodes, more TCP sessions reaching high congestion window values, and no single-core bottle necks in multicore equipment. Tuning BIG-IP for High Performance S3 Service Delivery The F5 BIG-IP offers a set of different profiles it can run its Local Traffic Manager (LTM) module in accordance with; LTM is the heart of the server load balancing function. The most performant profile in terms of attainable traffic load is the “FastL4” profile. This, and other profiles such as “OneConnect” or “FastHTTP”, can be tied to a virtual server, and details around each profile can be found here within the BIG-IP GUI: The FastL4 profile can increase virtual server performance and throughput for supported platforms by using the embedded Packet Velocity Acceleration (ePVA) chip to accelerate traffic. The ePVA chip is a hardware acceleration field programmable gate array (FPGA) that delivers high-performance L4 throughput by offloading traffic processing to the hardware acceleration chip. The BIG-IP makes flow acceleration decisions in software and then offloads eligible flows to the ePVA chip for that acceleration. For platforms that do not contain the ePVA chip, the system performs acceleration actions in software. Software-only solutions can increase performance in direct relationship to the hardware offered by the underlying host. As examples of BIG-IP virtual edition (VE) software running on mid-grade hardware platforms, results with Dell can be found here and similar experiences with HPE Proliant platforms are here. One thing to note about FastL4 as the profile to underpin a performance mode BIG-IP virtual server is that it is layer 4 oriented. For certain features that involve layer 7 HTTP related fields, such as using iRules to swap HTTP headers or perform HTTP authentication, a different profile might be more suitable. A bonus of FastL4 are some interesting specific performance features catering to it. In the BIG-IP version 17 release train, there is a feature to quickly tear down, with no delay, TCP sessions no longer required. Most TCP stacks implement TCP “2MSL” rules, where upon receiving and sending TCP FIN messages, the socket enters a lengthy TCP “TIME_WAIT” state, often minutes long. This stems back to historically bad packet loss environments of the very early Internet. A concern was high latency and packet loss might see incoming packets arrive at a target very late, and the TCP state machine would be confused if no record of the socket still existed. As such, the lengthy TIME_WAIT period was adopted even though this is consuming on-board resources to maintain the state. With FastL4, the “fast” close with TCP reset option now exists, such that any incoming TCP FIN message observed by BIG-IP will result in TCP RESETS being sent to both endpoints, normally bypassing TIME_WAIT penalties. OneConnect and FastHTTP Profiles As mentioned, other traffic profiles on BIG-IP are directed towards Layer 7 and HTTP features. One interesting profile is F5’s “OneConnect”. The OneConnect feature set works with HTTP Keep-Alives, which allows the BIG-IP system to minimize the number of server-side TCP connections by making existing connections available for reuse by other clients. This reduces, among other things, excessive TCP 3-way handshakes (Syn, Syn-Ack, Ack) and mitigates the small TCP congestion windows that new TCP sessions start with and only increases with successful traffic delivery. Persistent server-side TCP connections ameliorate this. When a new connection is initiated to the virtual server, if an existing server-side flow to the pool member is idle, the BIG-IP system applies the OneConnect source mask to the IP address in the request to determine whether it is eligible to reuse the existing idle connection. If it is eligible, the BIG-IP system marks the connection as non-idle and sends a client request over it. If the request is not eligible for reuse, or an idle server-side flow is not found, the BIG-IP system creates a new server-side TCP connection and sends client requests over it. The last profile considered is the “Fast HTTP” profile. The Fast HTTP profile is designed to speed up certain types of HTTP connections and again strives to reduce the number of connections opened to the back-end HTTP servers. This is accomplished by combining features from the TCP, HTTP, and OneConnect profiles into a single profile that is optimized for network performance. A resulting high performance HTTP virtual server processes connections on a packet-by-packet basis and buffers only enough data to parse packet headers. The performance HTTP virtual server TCP behavior operates as follows: the BIG-IP system establishes server-side flows by opening TCP connections to pool members. When a client makes a connection to the performance HTTP virtual server, if an existing server-side flow to the pool member is idle, the BIG-IP LTM system marks the connection as non-idle and sends a client request over the connection. Summary The NetApp StorageGRID multi-node S3 compatible object storage solution fits well with a high-performance server load balancer, thus making the F5 BIG-IP a good fit. S3 protocol can itself be adjusted to improve transaction response times, such as through the use of multi-part uploads and downloads, amplifying the default load balancing to now spread even more traffic chunks over many NetApp nodes. BIG-IP has numerous approaches to configuring virtual servers, from highest performance L4-focused profiles to similar offerings that retain L7 HTTP awareness. Lab testing was accomplished using the S3Browser utility and results of traffic flows were confirmed with both the standard BIG-IP GUI and the additional AVR analytics module, which provides additional protocol insight.
Steve_Gorman
Jan 21, 2025 Place Technical Articles
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Use Fully Qualified Domain Name (FQDN) for GSLB Pool Member with F5 DNS
Normally, we define a specific IP (and port) to be used as GSLB pool member. This article provides a custom configuration to be able to use Fully Qualified Domain Name (FQDN) as GSLB pool member--with all GSLB features like health-check monitoring, load balancing method, persistence, etc. Despite GSLB as a mechanism to distribute traffic across datacenters having reached years of age, it has not become less relevant this recent years. The fact that internet infrastructure still rely heavily on DNS technology means GSLB is continuously used due to is lightweight nature and smooth integration. When using F5 DNS as GSLB solution, usually we are dealing with LTM and its VS as GSLB server and pool member respectively. Sometimes, we will add a non-LTM node as a generic server to provide inter-DC load balancing capability. Either way, we will end up with a pair of IP and port to represent the application, in which we sent a health-check against. Due to the trend of public cloud and CDN, there is a need to use FQDN as GSLB pool member (instead of IP and port pair). Some of us may immediately think of using a CNAME-type GSLB pool to accommodate this. However, there is a limitation in which BIG-IP requires a CNAME-type GSLB pool to use a wideIP-type pool member, in which we will end up with an IP and port pair (again!) We can use "static target", but there is "side-effect" where the pool member will always consider available (which then triggers the question why we need to use GSLB in the first place!). Additionally, F5 BIG-IP TMUI accepts FQDN input when we configure GSLB server and pool member. However, it will immediately translate to IP based on configured DNS. Thus, this is not the solution we are looking for Now this is where F5’s BIG-IP power (a.k.a programmability) comes into play. Enter the realm of customization... We all love customization, but at the same time do not want that to be overly complicated so that life becomes harder on day-2 🙃. Thus, the key is to use some customization, but simple enough to avoid unnecessary complication. Here is one idea to solve our FQDN as GSLB pool problem above The customized configuration object includes 1. External health-check monitor: Dynamically resolve DNS to translate FQDN into IP address Perform health-check monitoring against current IP address Result is used to determine GSLB pool member availability status 2. DNS iRules: Check #1: Checks if GSLB pool attached to wideIP contains only FQDN-type member (e.g. other pool referring to LTM VS is also attached to the wideIP) If false, do nothing (let DNS response refer to LTM VS) Otherwise, perform check #2 Check #2: Checks current health-check status of requested domain name If FQDN is up, modify DNS response to return current IP of FQDN Otherwise, perform fallback action as requirement (e.g. return empty response, return static IP, use fallback pool, etc.) 3. Internal Datagroup: Store current IP of FQDN, updated according to health-check interval Datagroup record value contains current IP if health-check success. Otherwise, the value contains empty data Here are some of the codes, where configured; wideIP is gslb.test.com, while GSLB pool member FQDN is arcadia.f5poc.id 1. External health-check monitor config gtm monitor external gslb_external_monitor { defaults-from external destination *:* interval 10 probe-timeout 5 run /Common/gslb_external_monitor_script timeout 120 #define FQDN here user-defined fqdn arcadia.f5poc.id } External health-check monitor script #!/bin/sh pidfile="/var/run/$MONITOR_NAME.$1..$2.pid" if [ -f $pidfile ] then kill -9 -`cat $pidfile` > /dev/null 2>&1 fi echo "$$" > $pidfile # Obtain current IP for the FQDN resolv=`dig +short ${fqdn}` # The actual monitoring action here curl -fIs -k https://${fqdn}/ --resolve ${fqdn}:443:${resolv} | grep -i HTTP 2>&1 > /dev/null status=$? if [ $status -eq 0 ] then # Actions when health-check success rm -f $pidfile tmsh modify ltm data-group internal fqdn { records replace-all-with { $fqdn { data $resolv } } } echo "sending monitor to ${fqdn} ${resolv} with result OK" | logger -p local0.info echo "up" else # Actions when health-check fails tmsh modify ltm data-group internal fqdn { records replace-all-with { $fqdn { } } } echo "sending monitor to ${fqdn} ${resolv} with result NOK" | logger -p local0.info fi rm -f $pidfile 2. DNS iRules when DNS_REQUEST { set qname [DNS::question name] # Obtain current IP for the FQDN set currentip [class match -value $qname equals fqdn] } when DNS_RESPONSE { set rname [getfield [lindex [split [DNS::answer]] 4] "\}" 1 ] #Check if return is IP address of specially encoded FQDN IP, 10.10.10.10 in this example if {$rname eq "10.10.10.10" }{ #Response is only from pool with external monitor, meaning no other pool is attached to wideIP if {$currentip ne ""}{ #Current FQDN health-check success DNS::answer clear # Use current IP to construct DNS answer section DNS::answer insert "[DNS::question name]. 123 [DNS::question class] [DNS::question type] $currentip" } else { #Current FQDN health-check failed #Define action to be performed here DNS::answer clear } } } 3. Internal Datagroup ltm data-group internal fqdn { records { # Define FQDN as record name arcadia.f5poc.id { # Record data contains IP, where this will be continuously updated by external monitoring script data 158.140.176.219 } } type string } *GSLB virtual server configuration Some testing The resolve will follow whichever current IP address for the FQDN. If a returning CNAME response is required, you can do so by modifying DNS irules above. The logic and code are open to any improvement, so leave your suggestions in the comments if you have any. Thanks!
Doddy_ZUHRI
Nov 23, 2024 Place Technical Articles
483Views
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In turn, KubeVirt is based on the KVM/QEMU projects. VMs running in OpenShift Virtualization can be plugged into the POD network and/or in external networks, being able to replace a VMware platform. \n This article has an attached guide (PDF) that shows how to move workloads and BIG-IP Virtual Edition (VE) instances from VMware with vSphere networking to OpenShift Virtualization. This guide is aligned with the OpenShift Virtualization Reference Implementation Guide and the OpenShift Migration Toolkit for Virtualization. The latter facilitates the migration of application VMs. \n \n Overview of the guide \n The guide is composed of the following sections: \n \n Overview of the migration process \n Network considerations \n Preparatory works \n Migration procedure \n Additional information \n \n The guide is meant to be self-contained and includes all the details needed. In https://github.com/f5devcentral/f5-bd-openshift-virt-migration/tree/main you will find sample manifests to be used alongside the guide. \n Note that migrating a BIG-IP VE from VMware to OpenShift Virtualization doesn’t require any additional licenses. The existing licenses can be re-used. \n Note also that OpenShift Virtualization requires the Q35 machine type, which is supported starting with BIG-IP versions 15.1.0, 16.1.5, and 17.1.1. Check this link for details. \n Final remarks and next steps \n OpenShift Virtualization provides a strong foundation to host both VM and container workloads, thus unifying the platform for all applications and ultimately simplifying the infrastructure. \n Download the attached PDF to see the guide. This guide is a living document, and we expect to update it with improved content. We are looking forward to your questions and comments on the guide. ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"1943","kudosSumWeight":5,"repliesCount":2,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[{"__typename":"VideoEdge","cursor":"MHxodHRwczovL3d3dy55b3V0dWJlLmNvbS93YXRjaD92PUROTmNRcGc3MENFLzE3NDMwMDA2NjExMjd8MHwyNTsyNXx8","node":{"__typename":"AssociatedVideo","videoTag":{"__typename":"VideoTag","vid":"https://www.youtube.com/watch?v=DNNcQpg70CE/1743000661127","thumbnail":"https://i.ytimg.com/vi/DNNcQpg70CE/hqdefault.jpg","uploading":false,"height":240,"width":320,"title":null},"videoAssociationType":"INLINE_BODY"}}],"totalCount":1,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}}},"Conversation:conversation:340273":{"__typename":"Conversation","id":"conversation:340273","topic":{"__typename":"TkbTopicMessage","uid":340273},"lastPostingActivityTime":"2025-03-18T09:30:02.944-07:00","solved":false},"User:user:417402":{"__typename":"User","uid":417402,"login":"Chad_Davis","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://community.f5.com/t5/s/zihoc95639/images/dS00MTc0MDItQ1EweDd5?image-coordinates=0%2C89%2C2400%2C2490"},"id":"user:417402"},"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zNDAyNzMta3VhRWJY?revision=15\"}":{"__typename":"AssociatedImage","url":"https://community.f5.com/t5/s/zihoc95639/images/bS0zNDAyNzMta3VhRWJY?revision=15","title":"image.png","associationType":"BODY","width":975,"height":558,"altText":""},"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zNDAyNzMtR3p3WFNV?revision=15\"}":{"__typename":"AssociatedImage","url":"https://community.f5.com/t5/s/zihoc95639/images/bS0zNDAyNzMtR3p3WFNV?revision=15","title":"clipboard_image-2-1741981786103.png","associationType":"BODY","width":1289,"height":781,"altText":""},"TkbTopicMessage:message:340273":{"__typename":"TkbTopicMessage","subject":"Introducing the F5 Application Study Tool (AST)","conversation":{"__ref":"Conversation:conversation:340273"},"id":"message:340273","revisionNum":15,"uid":340273,"depth":0,"board":{"__ref":"Tkb:board:TechnicalArticles"},"author":{"__ref":"User:user:417402"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" In the ever-evolving world of application delivery and security, gaining actionable insights into your infrastructure and applications has become more critical than ever. The Application Study Tool (AST) is designed to help technical teams and administrators leverage the power of open-source telemetry and visualization tools to enhance their monitoring, diagnostics, and analysis workflows. ","introduction":"","metrics":{"__typename":"MessageMetrics","views":1116},"postTime":"2025-03-18T09:30:02.944-07:00","lastPublishTime":"2025-03-18T09:30:02.944-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" For application delivery and security, the ability to quickly take concrete actions to improve infrastructure is more important than ever. F5 users should try the new Application Study Tool (AST). \n AST is designed to help technical teams and administrators use open-source telemetry and visualization tools to improve their monitoring, diagnosis, and analysis workflows. Whether you’re trying to pinpoint root causes for outages or streamline BIG-IP fleet management, the Application Study Tool provides a foundation for powerful, customizable insights. \n In this article, you will learn the basics of the Application Study Tool, AST's key features, and how it can provide value to your organization. \n \n \n What is the Application Study Tool? \n The Application Study Tool is a lightweight, modular tool designed to integrate seamlessly with your BIG-IP ecosystem. AST uses modern open-source tools to track and visualize data. These tools include OpenTelemetry, Prometheus, and Grafana. This allows users to get more information about their BIG-IP deployments without heavily investing in new infrastructure. \n Application Study Tool is a simple, easy-to-use solution that can be used for quick Day 2 operations or to start a more complete telemetry solution. It can be used to observe at the speed and depth your organization needs. \n \n Why use AST? \n BIG-IP fleets have always been important for application delivery. But traditional telemetry options often don’t give deep, customizable analysis and visualization. Here are the challenges AST aims to address: \n \n Simplified Root Cause Analysis: Quickly identify bottlenecks, track performance metrics, and debug outages across your infrastructure. \n Day 2 Operations Made Easy: Analyze application behavior, usage patterns, and other telemetry data at scale for informed troubleshooting. \n Customizable Insights: Move beyond static, rigid monitoring tools with open-source dashboards and integrations tailored to your unique requirements. \n Seamless Integration: While BIG-IQ is excellent for configuration and backup management, AST bridges the gap to provide deep analytics and visualization. \n \n The Application Study Tool leverages open-source components to help F5 users analyze, view, and improve applications. \n \n How does AST work? \n The Application Study Tool brings together popular open-source tools to unlock powerful insights: \n \n OpenTelemetry Collector: \n \n Fetches enhanced BIG-IP metrics via iControlREST API calls. \n Collects telemetry data from your fleet for detailed analysis. \n \n Prometheus: \n \n Stores collected metrics in a highly efficient time-series database. \n Enables deep querying for actionable analysis. \n \n Grafana: \n \n Pre-configured dashboards provide out-of-the-box insights. \n Offers customizable visualizations for device-level and fleet-level data. \n \n Docker: \n \n Enables rapid deployment of AST in a containerized environment. \n \n \n \n Application Study Tool Use Cases \n The Application Study Tool is designed to tackle key challenges by addressing the following use cases: \n \n Visualizing BIG-IP Telemetry Data: Gain deep insights into your BIG-IP fleet with pre-built dashboards and configurable charts. \n API Endpoint Discovery & Analysis (LADT): \n \n Detect commonly used API endpoints in your ecosystem. \n Understand whether these APIs are authenticated and how often they’re accessed. \n \n Proactively Tackling Outages: Analyze metrics and logs to pinpoint the root cause of application issues before they escalate. \n \n Additionally, AST provides a foundation to enhance your setup for production-grade monitoring with high-availability, security features (For example., Grafana OIDC integration), and more. \n \n Features of the Local API Discovery Tool (LADT) \n The Local API Discovery Tool (LADT) is an integral part of this solution, providing insights into API-level usage across applications hosted on BIG-IP. Its core functionality includes: \n \n Batch Analysis of HTTP Data: LADT processes per-HTTP request data, providing detailed insights into API usage patterns. \n API Endpoint Discovery: Automatically identify likely API endpoints on BIG-IP, categorize them by authentication type, and track usage statistics. \n Lightweight Deployment: A Docker-ready solution that requires just a Linux system, BIG-IP’s telemetry data, and network connectivity. \n \n By using LADT, you can take API endpoint monitoring and analysis to the next level, even in environments with complex BIG-IP deployments. \n \n Local API Discovery – System Overview Visualized \n \n \n \n Prerequisites for running Application Study Tool \n Before beginning your journey with AST, ensure that your environment meets the following requirements: \n \n Infrastructure: A Linux VM with at least 4vCPU, 16GB RAM, and 200GB of available storage. \n BIG-IP Devices: Access to one or more BIG-IPs to collect telemetry data. Any current version of BIG-IP is supported. \n Network Connectivity: Ensure connectivity between the BIG-IP devices and the AST deployment for telemetry data collection. \n \n \n Why Upload Metrics to F5 Data Fabric? \n The F5 Data Fabric integration improves your telemetry experience by adding AI-driven security insights and advanced proactive threat detection. By uploading metrics to the F5 Data Fabric, you can: \n \n Gain actionable intelligence about potential threats in near-real-time. \n Bolster your defensive strategies with AI-driven analysis. \n Provide seamless access to your data when collaborating with your F5 Account Team or SEs. \n \n This collaboration allows F5 to better understand your environment and provide tailored recommendations specific to your organization’s needs. \n \n Getting Started with Application Study Tool \n Deployment of AST is straightforward. As a containerized solution, you can spin up the necessary stack quickly using Docker Compose on any standard Linux VM. Once deployed, the pre-configured Grafana dashboards will give you instant insights into your BIG-IP infrastructure. \n For example, you can start visualizing metrics like: \n \n Application traffic flow \n System/resource utilization \n API request behaviors \n \n Your customization possibilities don’t stop there. With OpenTelemetry’s flexible architecture and Grafana’s customizable dashboards, AST ensures you can tailor everything to your exact needs. \n \n Conclusion \n The Application Study Tool is a game-changer for organizations using F5 BIG-IP solutions. AST unlocks a new level of insight into BIG-IP environments by integrating open-source resources. This tool sets a strong foundation for observability and monitoring. \n Download the Application Study Tool and take the first step toward building a truly observable infrastructure. \n Need help getting started? \n \n Visit the official GitHub Repository for AST for deployment instructions \n See additional resources: F5 Application Study Tool Labs F5 AST Local API \n Check out JRahm's recent livestream DevCentral Connects: Application Study Tool \n \n Have you checked out the Application Study Tool? Tell us what you think below. 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keeping client IP addresses and egress flows","conversation":{"__ref":"Conversation:conversation:333352"},"id":"message:333352","revisionNum":6,"uid":333352,"depth":0,"board":{"__ref":"Tkb:board:TechnicalArticles"},"author":{"__ref":"User:user:303102"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" Controlling the egress traffic in OpenShift allows to use the BIG-IP for several use cases: \n \n Keeping the source IP of the ingress clients \n Providing highly scalable SNAT for egress flows \n Providing security functionalities for egress flows \n ","introduction":"","metrics":{"__typename":"MessageMetrics","views":428},"postTime":"2024-09-02T05:00:00.038-07:00","lastPublishTime":"2025-01-28T10:32:23.794-08:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Introduction \n In this article it will be covered how to control the egress traffic in the Red Hat OpenShift cluster making use of the AdminPolicyBasedExternalRoute resource type introduced with OpenShift 4.14. This functionality can be used for several use cases: \n \n Keeping the source IP of the ingress clients by setting the BIG-IP as default gateway for the namespaces which the BIG-IP provides load balancing. \n Providing highly scalable SNAT for egress flows to outside the cluster. This includes per namespace SNATs. \n Providing security functionalities for egress flows to outside the cluster, such as firewall, IDS/IPS, malware protection, reporting and analytics, data loss prevention, URL filtering and integration with third party security solutions by doing SSL/TLS inspection. \n \n Keeping the source IP of ingress clients \n By default OpenShift sends the traffic to the default gateway of the network where the OpenShift cluster is placed. In the case of ingress traffic to the applications, in order to make the traffic symmetric, making the return traffic go through the load balancer, it has been required to use SNAT in the load balancer. With the introduction AdminPolicyBasedExternalRoute this is no longer required, allowing the use the source IP to the workload PODs (in the case of 1-tier deployment) or to the in-cluster ingress controller (in the case of 2-tier deployments). The overall traffic flows in the platform would be as shown next: \n \n Using the feature is straight forward, we only have to specify the namespaces for which we want the BIG-IP to be the gateway and the floating IP of the BIG-IPs facing the OpenShift cluster. An example is shown next: \n apiVersion: k8s.ovn.org/v1 kind: AdminPolicyBasedExternalRoute metadata: name: meg-policy-common spec: from: namespaceSelector: matchLabels: meg: common nextHops: static: - ip: \"10.1.10.100\" \n The labels can be freely chosen, in this case it is chosen \"meg\" as label (meg standing multi-egress gateway) and the value \"common.\" In the next sections we will see further possibilities. \n Providing highly scalable SNAT for egress flows \n One egress SNAT for all namespaces handled by the BIG-IP \n Another use case of this functionality is to delegate the SNAT function into BIG-IP, this allows for highly scalability SNAT for egress flows, this is traffic initiated from the cluster to the outside which is not load balanced. The scalability benefits are two fold: \n \n BIG-IP has a carrier-grade NAT which allows high number of concurrent flows and great configuration flexibility. \n By doing the SNAT function in BIG-IP, it is possible to SNAT the traffic of all nodes to a single IP instead of assigning one SNAT address per node. This allows managing the traffic through firewalls easier because filters don´t need to be changed when nodes are added or removed. \n \n Besides of the above, recall that this configuration in the BIG-IP can be done, if needed, in a per namespace basis, as shown next. \n \n This setup requires a virtual server which typically is not modified and doesn´t need to be created with CIS (it could be created with CIS with an AS3 ConfigMap if desired). The configuration is fairly simple: \n \n From this configuration it is worth highlighting: \n \n Specifying the VS type as Forwarding (IP) \n Restricting the source IPs with the range of the PODs \n Specifying the desired SNAT pool \n \n Multiple egress SNATs for namespaces handled by the BIG-IP \n It might be desired to have different sets of SNATs depending on the namespaces, this can be used by external firewalls to differentiate between applications. This can be accomplished by means of having multiple virtual servers as shown next: \n \n \n In OpenShift, there will be a separate AdminPolicyBasedExternalRoute manifest for each set of namespaces where we want to apply separate SNAT pools. In the figure above, these manifests are the \"meg-policy-common\" shown above and a new \"meg-policy-deciated\" shown next: \n apiVersion: k8s.ovn.org/v1 kind: AdminPolicyBasedExternalRoute metadata: name: meg-policy-dedicated spec: from: namespaceSelector: matchLabels: meg: dedicated nextHops: static: - ip: \"10.1.10.200\" Each AdminPolicyBasedExternalRoute points to a different floating-IP in the BIG-IP. But the BIG-IP cannot differentiate in the forwarding VS in which floating-IP the traffic was received because all the IPs in a given VLAN use the same MAC address. Because of the latter, each virtual server needs to be listening in a different VLAN so the BIG-IP can differentiate from which namespace the traffic came from. As you might have noted, typically the OpenShift nodes only sit in a single VLAN. To solve this, dummy VLANs in the BIG-IP will be created connecting to the same OpenShift network but with different interfaces. Furthermore, in order to have the same subnet in these VLANs it will be needed to assign each VLAN to a different route domain. \n Overall, the setup will be as follows: \n \n In practice the configuration is very similar to the case where there is a single egress virtual server: \n \n Note that from the previous configuration we only had to add the route domain ID (in this case %10) to the IP addresses. The routing domains created (10 and 20) are shown next: \n \n The full configuration of one of these route domains is shown next as example. The only required parameter is to have as parent the route domain 0 because it will be used to reach the external VLAN. \n \n The self-IPs of the BIG-IP facing the OpenShift clusters have to be in the corresponding route domains: \n \n Note that it is required to have separate non-floating IP addresses as well. \n Providing security functionalities for egress flows \n Once the BIG-IP is the gateway of the applications, it can be used to add many functionalities. This is outlined in the next figure: \n \n \n These functionalities are delivered with the SSL Ochestrator and Secure Web Gateway Services add-on modules. \n Furthermore, these modules allow the inclusion of additional third-party security integrations, like Cisco Firepower, Cisco WSA, Symantec DLP, RSA Netwitness, FireEye NX and PaloAlto NG Firewall. \n Conclusion and final remarks \n The AdminPolicyBasedExternalRoute resource type introduced with OpenShift 4.14 opens many possibilities of getting more visibility into the cluster, higher scalability and many security functionalities available in your existing BIG-IP platform. I hope this article has been an eye opener for the possibilities of the BIG-IP platform with OpenShift. I would love to hear if you have any specific requirements for the use cases mentioned in the article. 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This license can be purchased from your authorized F5 vendor. If you do not have an F5 vendor, and you are in either Canada or the US you can purchase a lab license online: \n \n CDW BIG-IP Virtual Edition Lab License \n CDW Canada BIG-IP Virtual Edition Lab License \n \n Once completed, the order is sent to F5 for fulfillment and your license will be delivered shortly after via e-mail. F5 is investigating ways to improve this process. \n To download the BIG-IP Virtual Edition, log into my.f5.com (separate login from DevCentral), navigate down to the Downloads card under the Support Resources section of the page. Select BIG-IP from the product group family and then the current version of BIG-IP. \n You will be presented with a list of options, at the bottom, select the Virtual-Edition option that has the following descriptions: \n \n For VMware Fusion or Workstation or ESX/i: Image fileset for VMware ESX/i Server \n For Microsoft HyperV: Image fileset for Microsoft Hyper-V \n KVM RHEL/CentoOS: Image file set for KVM Red Hat Enterprise Linux/CentOS \n \n Note: There are also 1 Slot versions of the above images where a 2nd boot partition is not needed for in-place upgrades. These images include _1SLOT- to the image name instead of ALL. \n The below guides will help get you started with F5 BIG-IP Virtual Edition to develop for VMWare Fusion, AWS, Azure, VMware, or Microsoft Hyper-V. These guides follow standard practices for installing in production environments and performance recommendations change based on lower use/non-critical needs for development or lab environments. Similar to driving a tank, use your best judgement. \n \n Deploying F5 BIG-IP Virtual Edition on VMware Fusion \n Deploying F5 BIG-IP in Microsoft Azure for Developers \n Deploying F5 BIG-IP in AWS for Developers \n Deploying F5 BIG-IP in Windows Server Hyper-V for Developers \n Deploying F5 BIG-IP in VMware vCloud Director and ESX for Developers \n \n Note: F5 Support maintains authoritative Azure, AWS, Hyper-V, and ESX/vCloud installation documentation. VMware Fusion is not an official F5-supported hypervisor so DevCentral publishes the Fusion guide with the help of our Field Systems Engineering teams. 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Configuration to Support Duo MFA using iRule","conversation":{"__ref":"Conversation:conversation:283971"},"id":"message:283971","revisionNum":9,"uid":283971,"depth":0,"board":{"__ref":"Tkb:board:TechnicalArticles"},"author":{"__ref":"User:user:214133"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":"","introduction":"","metrics":{"__typename":"MessageMetrics","views":17920},"postTime":"2021-03-03T08:35:12.000-08:00","lastPublishTime":"2025-03-03T09:35:29.618-08:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Overview \n BIG-IP APM has supported Duo as an MFA provider for a long time with RADIUS-based integration. Recently, Duo has added support for Universal Prompt that uses Open ID Connect (OIDC) protocol to provide two-factor authentication. To integrate APM as an OIDC client and resource server, and Duo as an Identity Provider (IdP), Duo requires the user’s logon name and custom parameters to be sent for Authentication and Token request. \n This guide describes the configuration required on APM to enable Duo MFA integration using an iRule. iRules addresses the custom parameter challenges by generating the needed custom values and saving them in session variables, which the OAuth Client agent then uses to perform MFA with Duo. This integration procedure is supported on BIG-IP versions 13.1, 14.1x, 15.1x, and 16.x. \n To integrate Duo MFA with APM, complete the following tasks: \n 1. Choose deployment type: Per-request or Per-session \n 2. Configure credentials and policies for MFA on the DUO web portal \n 3. Create OAuth objects on the BIG-IP system \n 4. Configure the iRule \n 5. Create the appropriate access policy/policies on the BIG-IP system \n 6. Apply policy/policies and iRule to the APM virtual server \n Choose deployment type \n APM supports two different types of policies for performing authentication functions. \n \n Per-session policies: Per-session policies provide authentication and authorization functions that occur only at the beginning of a user’s session. These policies are compatible with most APM use cases such as VPN, Webtop portal, Remote Desktop, federation IdP, etc. \n Per-request policies: Per-request policies provide dynamic authentication and authorization functionality that may occur at any time during a user’s session, such as step-up authentication or auditing functions only for certain resources. These policies are only compatible with Identity Aware Proxy and Web Access Management use cases and cannot be used with VPN or webtop portals. \n \n This guide contains information about setting up both policy types. \n Prerequisites \n Ensure the BIG-IP system has DNS and internet connectivity to contact Duo directly for validating the user's OAuth tokens. \n Configure credentials and policies for MFA on Duo web portal \n Before you can protect your F5 BIG-IP APM Web application with Duo, you will first need to sign up for a Duo account. \n 1. Log in to the Duo Admin Panel and navigate to Applications. \n 2. Click Protect an application. \n Figure 1: Duo Admin Panel – Protect an Application \n 3. Locate the entry for F5 BIG-IP APM Web in the applications list and click Protect to get the Client ID, Client secret, and API hostname. You will need this information to configure objects on APM. \n Figure 2: Duo Admin Panel – F5 BIG-IP APM Web \n 4. As DUO is used as a secondary authentication factor, the user’s logon name is sent along with the authentication request. Depending on your security policy, you may want to pre-provision users in Duo, or you may allow them to self-provision to set their preferred authentication type when they first log on. \n To add users to the Duo system, navigate to the Dashboard page and click the Add New... -> Add User button. A Duo username should match the user's primary authentication username. Refer to the https://duo.com/docs/enrolling-users link for the different methods of user enrollment. \n Refer to Duo Universal Prompt for additional information on Duo’s two-factor authentication. \n Create OAuth objects on the BIG-IP system \n Create a JSON web key \n When APM is configured to act as an OAuth client or resource server, it uses JSON web keys (JWKs) to validate the JSON web tokens it receives from Duo. \n To create a JSON web key: \n 1. On the Main tab, select Access > Federation > JSON Web Token > Key Configuration. \n The Key Configuration screen opens. \n 2. To add a new key configuration, click Create. \n 3. In the ID and Shared Secret fields, enter the Client ID and Client Secret values respectively obtained from Duo when protecting the application. \n 4. In the Type list, select the cryptographic algorithm used to sign the JSON web key. \n Figure 3: Key Configuration screen \n 5. Click Save. \n Create a JSON web token \n As an OAuth client or resource server, APM validates the JSON web tokens (JWT) it receives from Duo. \n To create a JSON web token: \n 1. On the Main tab, select Access > Federation > JSON Web Token > Token Configuration. \n The Token Configuration screen opens. \n 2. To add a new token configuration, click Create. \n 3. In the Issuer field, enter the API hostname value obtained from Duo when protecting the application. \n 4. In the Signing Algorithms area, select from the Available list and populate the Allowed and Blocked lists. \n 5. In the Keys (JWK) area, select the previously configured JSON web key in the allowed list of keys. \n Figure 4: Token Configuration screen \n 6. Click Save. \n Configure Duo as an OAuth provider \n APM uses the OAuth provider settings to get URIs on the external OAuth authorization server for JWT web tokens. \n To configure an OAuth provider: \n 1. On the Main tab, select Access > Federation > OAuth Client / Resource Server > Provider. \n The Provider screen opens. \n 2. To add a provider, click Create. \n 3. In the Name field, type a name for the provider. \n 4. From the Type list, select Custom. \n 5. For Token Configuration (JWT), select a configuration from the list. \n 6. In the Authentication URI field, type the URI on the provider where APM should redirect the user for authentication. The hostname is the same as the API hostname in the Duo application. \n 7. In the Token URI field, type the URI on the provider where APM can get a token. The hostname is the same as the API hostname in the Duo application. \n Figure 5: OAuth Provider screen \n 8. Click Finished. \n Configure Duo server for APM \n The OAuth Server settings specify the OAuth provider and role that Access Policy Manager (APM) plays with that provider. It also sets the Client ID, Client Secret, and Client’s SSL certificates that APM uses to communicate with the provider. \n To configure a Duo server: \n 1. On the Main tab, select Access > Federation > OAuth Client / Resource Server > OAuth Server. \n The OAuth Server screen opens. \n 2. To add a server, click Create. \n 3. In the Name field, type a name for the Duo server. \n 4. From the Mode list, select how you want the APM to be configured. \n 5. From the Type list, select Custom. \n 6. From the OAuth Provider list, select the Duo provider. \n 7. From the DNS Resolver list, select a DNS resolver (or click the plus (+) icon, create a DNS resolver, and then select it). \n 8. In the Token Validation Interval field, type a number. \n In a per-request policy subroutine configured to validate the token, the subroutine repeats at this interval or the expiry time of the access token, whichever is shorter. \n 9. In the Client Settings area, paste the Client ID and Client secret you obtained from Duo when protecting the application. \n 10. From the Client's ServerSSL Profile Name, select a server SSL profile. \n Figure 6: OAuth Server screen \n 11. Click Finished. \n Configure an auth-redirect-request and a token-request \n Requests specify the HTTP method, parameters, and headers to use for the specific type of request. An auth-redirect-request tells Duo where to redirect the end-user, and a token-request accesses the authorization server for obtaining an access token. \n To configure an auth-redirect-request: \n 1. On the Main tab, select Access > Federation > OAuth Client / Resource Server > Request. \n The Request screen opens. \n 2. To add a request, click Create. \n 3. In the Name field, type a name for the request. \n 4. For the HTTP Method, select GET. \n 5. For the Type, select auth-redirect-request. \n 6. As shown in Figure 7, specify the list of GET parameters to be sent: \n \n request parameter with value depending on the type of policy \n For per-request policy: %{subsession.custom.jwt_duo} \n For per-session policy: %{session.custom.jwt_duo} \n client_id parameter with type client-id \n response_type parameter with type response-type \n \n Figure 7: Request screen with auth-redirect-request (Use “subsession.custom…” for Per-request or “session.custom…” for Per-session) \n 7. Click Finished. \n To configure a token-request: \n 1. On the Main tab, select Access > Federation > OAuth Client / Resource Server > Request. \n The Request screen opens. \n 2. To add a request, click Create. \n 3. In the Name field, type a name for the request. \n 4. For the HTTP Method, select POST. \n 5. For the Type, select token-request. \n 6. As shown in Figure 8, specify the list of POST parameters to be sent: \n \n client_assertion parameter with value depending on the type of policy \n For per-request policy: %{subsession.custom.jwt_duo_token} \n For per-session policy: %{session.custom.jwt_duo_token} \n client_assertion_type parameter with value urn:ietf:params:oauth:client-assertion-type:jwt-bearer \n grant_type parameter with type grant-type \n redirect_uri parameter with type redirect-uri \n \n Figure 8: Request screen with token-request (Use “subsession.custom…” for Per-request or “session.custom…” for Per-session) \n 7. Click Finished. \n Configure the iRule \n iRules gives you the ability to customize and manage your network traffic. Configure an iRule that creates the required sub-session variables and usernames for Duo integration. \n Note: This iRule has sections for both per-request and per-session policies and can be used for either type of deployment. \n To configure an iRule: \n 1. On the Main tab, click Local Traffic > iRules. \n 2. To create an iRules, click Create. \n 3. In the Name field, type a name for the iRule. \n 4. Copy the sample code given below and paste it in the Definition field. \n Replace the following variables with values specific to the Duo application: \n \n <Duo Client ID> in the getClientId function with Duo Application ID. \n <Duo API Hostname> in the createJwtToken function with API Hostname. For example, https://api-duohostname.com/oauth/v1/token. \n <JSON Web Key> in the getJwkName function with the configured JSON web key. \n \n Note: The iRule ID here is set as JWT_CREATE. You can rename the ID as desired. You specify this ID in the iRule Event agent in Visual Policy Editor. \n when ACCESS_POLICY_AGENT_EVENT {\n if { [ACCESS::policy agent_id] eq \"JWT_CREATE\" } {\n set duo_uname [ACCESS::session data get \"session.logon.last.username\"]\n\n # Inline logic for creating JWT\n set header \"{\\\"alg\\\":\\\"HS512\\\",\\\"typ\\\":\\\"JWT\\\"}\"\n set exp [expr {[clock seconds] + 900}]\n set client_id \"<Duo Client ID>\"\n\n set redirect_uri \"https://[ACCESS::session data get session.server.network.name]/oauth/client/redirect\"\n set payload \"{\\\"response_type\\\": \\\"code\\\",\\\"scope\\\":\\\"openid\\\",\\\"exp\\\":${exp},\\\"client_id\\\":\\\"${client_id}\\\",\\\"redirect_uri\\\":\\\"${redirect_uri}\\\",\\\"duo_uname\\\":\\\"${duo_uname}\\\"}\"\n\n set jwt_duo [ACCESS::oauth sign -header $header -payload $payload -alg HS512 -key \"<JSON Web Key>\"]\n ACCESS::session data set session.custom.jwt_duo $jwt_duo\n\n # JWT Token creation\n set aud \"<Duo API Hostname>\"\n set jti [string range [clock seconds] 0 31]\n set token_payload \"{\\\"sub\\\": \\\"${client_id}\\\",\\\"iss\\\":\\\"${client_id}\\\",\\\"aud\\\":\\\"${aud}\\\",\\\"exp\\\":${exp},\\\"jti\\\":\\\"${jti}\\\"}\"\n\n set jwt_duo_token [ACCESS::oauth sign -header $header -payload $token_payload -alg HS512 -key \"<JSON Web Key>\"]\n ACCESS::session data set session.custom.jwt_duo_token $jwt_duo_token\n }\n}\n\nwhen ACCESS_PER_REQUEST_AGENT_EVENT {\n if { [ACCESS::perflow get perflow.irule_agent_id] eq \"JWT_CREATE\" } {\n set duo_uname [ACCESS::session data get \"session.logon.last.username\"]\n\n set header \"{\\\"alg\\\":\\\"HS512\\\",\\\"typ\\\":\\\"JWT\\\"}\"\n set exp [expr {[clock seconds] + 900}]\n set client_id \"<Duo Client ID>\"\n\n set redirect_uri \"https://[ACCESS::session data get session.server.network.name]/oauth/client/redirect\"\n set payload \"{\\\"response_type\\\": \\\"code\\\",\\\"scope\\\":\\\"openid\\\",\\\"exp\\\":${exp},\\\"client_id\\\":\\\"${client_id}\\\",\\\"redirect_uri\\\":\\\"${redirect_uri}\\\",\\\"duo_uname\\\":\\\"${duo_uname}\\\"}\"\n\n set jwt_duo [ACCESS::oauth sign -header $header -payload $payload -alg HS512 -key \"<JSON Web Key>\"]\n ACCESS::perflow set perflow.custom $jwt_duo\n\n # JWT Token creation\n set aud \"<Duo API Hostname>\"\n set jti [string range [clock seconds] 0 31]\n set token_payload \"{\\\"sub\\\": \\\"${client_id}\\\",\\\"iss\\\":\\\"${client_id}\\\",\\\"aud\\\":\\\"${aud}\\\",\\\"exp\\\":${exp},\\\"jti\\\":\\\"${jti}\\\"}\"\n\n set jwt_duo_token [ACCESS::oauth sign -header $header -payload $token_payload -alg HS512 -key \"<JSON Web Key>\"]\n ACCESS::perflow set perflow.scratchpad $jwt_duo_token\n }\n} \n Note: iRule updated 11/27/2024 to eliminate CMP demotion. \n \n Figure 9: iRule screen \n 5. Click Finished. \n Create the appropriate access policy/policies on the BIG-IP system \n Per-request policy \n Skip this section for a per-session type deployment \n The per-request policy is used to perform secondary authentication with Duo. Configure the access policies through the access menu, using the Visual Policy Editor. The per-request access policy must have a subroutine with an iRule Event, Variable Assign, and an OAuth Client agent that requests authorization and tokens from an OAuth server. You may use other per-request policy items such as URL branching or Client Type to call Duo only for certain target URIs. \n Figure 10 shows a subroutine named duosubroutine in the per-request policy that handles Duo MFA authentication. \n Figure 10: Per-request policy in Visual Policy Editor \n Configuring the iRule Event agent \n The iRule Event agent specifies the iRule ID to be executed for Duo integration. In the ID field, type the iRule ID as configured in the iRule. \n Figure 11: iRule Event agent in Visual Policy Editor \n Configuring the Variable Assign agent \n The Variable Assign agent specifies the variables for token and redirect requests and assigns a value for Duo MFA in a subroutine. This is required only for per-request type deployment. Add sub-session variables as custom variables and assign their custom Tcl expressions as shown in Figure 12. \n \n subsession.custom.jwt_duo_token = return [mcget {perflow.scratchpad}] \n subsession.custom.jwt_duo = return [mcget {perflow.custom}] \n \n Figure 12: Variable Assign agent in Visual Policy Editor \n Configuring the OAuth Client agent \n An OAuth Client agent requests authorization and tokens from the Duo server. Specify OAuth parameters as shown in Figure 13. \n \n In the Server list, select the Duo server to which the OAuth client directs requests. \n In the Authentication Redirect Request list, select the auth-redirect-request configured earlier. \n In the Token Request list, select the token-request configured earlier. \n Some deployments may not need the additional information provided by OpenID Connect. You could, in that case, disable it. \n \n Figure 13: OAuth Client agent in Visual Policy Editor \n Per-session policy \n Configure the Per Session policy as appropriate for your chosen deployment type. \n \n Per-request: The per-session policy must contain at least one logon page to set the username variable in the user’s session. Preferably it should also perform some type of primary authentication. This validated username is used later in the per-request policy. \n Per-session: The per-session policy is used for all authentication. A per-request policy is not used. \n \n Figures 14a and 14b show a per-session policy that runs when a client initiates a session. Depending on the actions you include in the access policy, it can authenticate the user and perform actions that populate session variables with data for use throughout the session. \n Figure 14a: Per-session policy in Visual Policy Editor performs both primary authentication and Duo authentication (for per-session use case) \n Figure 14b: Per-session policy in Visual Policy Editor performs primary authentication only (for per-request use case) \n Apply policy/policies and iRule to the APM virtual server \n Finally, apply the per-request policy, per-session policy, and iRule to the APM virtual server. You assign iRules as a resource to the virtual server that users connect. Configure the virtual server’s default pool to the protected local web resource. \n Apply policy/policies to the virtual server \n Per-request policy \n To attach policies to the virtual server: \n 1. On the Main tab, click Local Traffic > Virtual Servers. \n 2. Select the Virtual Server. \n 3. In the Access Policy section, select the policy you created. \n 4. Click Finished. \n Figure 15: Access Policy section in Virtual Server (per-request policy) \n Per-session policy \n Figure 16 shows the Access Policy section in Virtual Server when the per-session policy is deployed. \n Figure 16: Access Policy section in Virtual Server (per-session policy) \n Apply iRule to the virtual server \n To attach the iRule to the virtual server: \n 1. On the Main tab, click Local Traffic > Virtual Servers. \n 2. Select the Virtual Server. \n 3. Select the Resources tab. \n 4. Click Manage in the iRules section. \n 5. Select an iRule from the Available list and add it to the Enabled list. \n 6. Click Finished. 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Cloud Security Service Insertion With BIG-IP Advanced WAF","conversation":{"__ref":"Conversation:conversation:293896"},"id":"message:293896","revisionNum":11,"uid":293896,"depth":0,"board":{"__ref":"Tkb:board:TechnicalArticles"},"author":{"__ref":"User:user:275378"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":"","introduction":"","metrics":{"__typename":"MessageMetrics","views":2136},"postTime":"2022-04-10T09:00:00.022-07:00","lastPublishTime":"2025-03-03T08:33:15.357-08:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" \n In this article we will show you how to quickly deploy and operate external services of your choice across multiple public clouds. For this article I will select the BIG-IP Advanced WAF (PAYG), future articles will cover additional solutions. \n \n Co-Author: Anitha Mareedu, Sr. Security Engineer, F5 \n \n Introduction \n F5’s Distributed Cloud Securtiy Service Insertion solution allows enterprises to deploy and operate external services of their choice across multiple public clouds. \n Let's start by looking at a real-world customer example. The enterprise has standardized on an external firewall in their private data center. Their network and security team are very familiar with using BIG-IP AWAF. They want to deploy the same security firewall solution that they use in the private datacenter in the public cloud. \n The requirements are: \n \n a simple operational model to deploy these services \n a unified security policy \n consistency across different clouds \n simple deployments \n unified logging \n \n Challenges \n Customers have identified several challenges in moving to the cloud. Initallly, teams that are very familiar with supporting services in their private data center usually do not have the expertise in designing, deploying and supporting in public clouds. If the same team then is tasked with deploying to multiple clouds the gap widens, terminology, archtitecture tools and constructs are all unique. \n Second, the operational models are different across different clouds. In AWS, you use either a VPC or a transit gateway (TGW), in Azure you use a VNET and Google has VPC’s. \n Solution Description \n Let's look at how F5’s Distributed Cloud Security Service insertion solution helps simplify and unify security solution deployments in multi-cloud and hybrid cloud environments: \n \n \n Infrastructure-as-code: Implementation and policy configuration can be automated and run as infrastructure-as-code across clouds and regions, allowing policies to be repeatable in any major public or private cloud. \n \n Easy setup and management: This simplified setup and management extends across AWS, Azure, and other clouds, as the F5 Distributed Cloud Platform supports AWS Transit Gateway, virtual network peering in Azure, and use of VPC attachments. \n Define once and replicate models: No extra handcrafting is needed for consistent, straightforward operations and deployment. \n Unified traffic steering rules: With the Distributed Cloud Platform, traffic is rerouted from networks through the security service using the same steering rules across different public and private clouds. Using F5 Distributed Cloud Console, IT pros get granular visibility and single-pane-of-glass management of traffic across clouds and networks. \n Optional policy deployment routes: Policies can be deployed at either or both the network layer (using IP addresses) or the application layer (using APIs). \n \n Diagram \n \n Step by Step Process \n This walk thru assumes you already have an AWS VPC deployed. Have handy the VPC id. \n \n Log into the F5 Distributed Cloud Dashboard \n \n You are presented with the Dashboard where you can choose which deployment option you want to work with. We will be working with Cloud and Edge Sites. \n \n Select Cloud and Edge Sites > Manage > Site Management > AWS TWG Sites \n Click Add the AWS Transit Gateway (TWG) \n \n \n Under Metadata give your TWG site a Name, Label and Description \n Click on Configure under AWS Configuration \n \n This brings up the Services VPC Configuration Page \n \n Select your AWS region \n Select Services VPC, leave as New, let it genetrate a name or choose your own name and give the Primary CIDR block you want to assign to the VPC. \n Leave Transit Gateway as New TWG \n Leave BGP as Automatic \n Under Site Node Parameters, Ingress/ Egress select “Add Item” \n \n \n Move slider on upper right corner to Show Advanced Fields \n \n \n Fill in required configuration, AWS AZ Name and CIDR Blocks for each of the the subnets and click the “Add Item” You can let the system autogenerate these or assign the desired range. \n \n This will take you back to the last screen, where you need to either create or select your cloud credentials. These are Programmatic Access Credentials allowing API access. \n \n Click Apply \n \n This takes you to the previous screen where we connect your current VPC to the Service VPC we are creating. (have VPC id available) \n Click Configure under VPC attachments \n \n Click Add Item \n Supply VPC id \n Click Apply \n \n This takes you back once again to the AWS TWG Site Screen. \n \n Finish with clicking Save and Exit. \n \n \n In the UI you will then click Apply. \n \n You are now deploying your new Security VPC via Terraform. \n While that is deploying we will move on to the External Services. \n \n Manage > Site Management > External Services > Add External Service \n \n \n Give your Service a name, add a label and description. \n Click “Configure” under Select NFV Service Provider. \n \n For this article we will select the F5 BIG-IP Advanced WAF (PAYG), future articles will cover additional solutions. \n \n Provide the Admin Password \n Admin Username \n public SSH Key that you will use to access your BIG-IP deployment. \n Select the TWG site you created above. \n Finally click “Add Item“ under Service Nodes. \n \n Service nodes \n \n Enter a Node name and the Avilibilty Zones you wish to delpoy into. Then click “Add Item” \n \n This will take you back to the original screen. \n \n Enable HTTPS Management of Nodes, supply a delegated doman that will issue a Certificate. \n Under Select Service Type” Keep Inside VIP at Automatic and Set the Outside VIP to “Advertise On Outside Network”. \n Finally Click “Save and Exit” \n \n At the end, the External Security Service is deployed, and you are taken to all the External Services. \n \n Click the name of the External Service you deployed to expand the details \n \n \n From this screen you are able to access several items, the two I want to point out are the TGW stats and the BIG-IP you deployed by clicking the Management Dashboard URL. \n \n Click under Site the TWG Service you deployed \n \n Here you are able to see fine grained stats under all the tabs. \n \n System Metrics \n Application Metrics \n Site Status \n Nodes \n Interfaces \n Alerts \n Requests \n Top Talkers \n Connections \n TWG \n Flow tables \n DHCP \n Status Objects \n Tools \n \n \n Going back click the hyperlink to the BIG-IP if you wish to look at the configuration. \n F5 Distributed Cloud Service Insertion automatically configured your BIG-IP with the following information: \n • Interfaces • Self IPs • Routes • Management and credentials • VLANs • IPoIP tunnel SI<-> BIG-IP • VIP \n The following two items will need to be configured on your BIG-IP. This configuration \n \n Configure AWAF policies \n SecOps can access familiar BIG-IP UI using management link provided in F5 Cloud Console and set up and configure AWAF ploicies \n Define a Traffic Steering Policy\n \n Network traffic to define traffic steering policy at Network (L3/L4) layer \n Service policy to define traffic steering policy at App(L7) level. \n Below are the traffic steering control methods available:\n \n Network level – Ip address, port, etc \n App level – API, Method, etc \n \n \n \n \n \n At the end of this step, you can see traffic getting diverted to BIG-IP and getting inspected by BIG-IP. \n Summary \n As you can see, F5 Distributed Cloud Security Service Insertion dramatically reduces the operation complexity for deploying external services in public clouds, it greatly enhances the security posture and it vastly improves productivity for all the operations teams such as NetOps, SecOps or DevOps. \n 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Imperative and Declarative Automation with F5 BIG-IP – AppWorld 2025","conversation":{"__ref":"Conversation:conversation:339856"},"id":"message:339856","revisionNum":2,"uid":339856,"depth":0,"board":{"__ref":"Tkb:board:TechnicalArticles"},"author":{"__ref":"User:user:195328"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":"","introduction":"","metrics":{"__typename":"MessageMetrics","views":216},"postTime":"2025-02-20T14:45:08.231-08:00","lastPublishTime":"2025-02-20T14:45:08.231-08:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" At AppWorld 2025, Anton Varkalevich and I will be hosting a hands-on lab, \"Mastering Imperative and Declarative Automation with F5 BIG-IP.\" \n I'm Matt Mabis, a Senior Solutions Architect in the Business Development/Technical Alliances department at F5. I specialize in solution integrations with our partnerships with Red Hat (Ansible and OpenShift), VMware, and Omnissa Horizon. \n Anton Varkalevich is a Solutions Engineer III for Financial Services at F5, specializing in ensuring financial customers have the right solutions to meet their unique needs. Automation plays a critical role in financial institutions, enabling them to serve their customers quickly and effectively. \n This lab is the result of years of experience working with customers to streamline application deployments on F5 BIG-IP. We’ll use Ansible Automation Platform to demonstrate both imperative and declarative automation approaches. \n \n Participants will first deploy common use cases—such as HTTPS and SSL-delivered applications—using imperative automation with individual Ansible modules. Then, we’ll achieve the same outcomes using declarative automation, offering a side-by-side comparison to help attendees choose the best approach for their needs. \n By the end of this lab, you’ll have a solid understanding of both automation styles and how to apply them effectively in your environment. \n Join us at AppWorld 2025—we look forward to sharing our knowledge with you! ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"1464","kudosSumWeight":0,"repliesCount":0,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuMnwyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zMzk4NTYtRWV6YkJZ?revision=2\"}"}}],"totalCount":1,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}}},"Conversation:conversation:289008":{"__typename":"Conversation","id":"conversation:289008","topic":{"__typename":"TkbTopicMessage","uid":289008},"lastPostingActivityTime":"2025-01-27T10:16:42.517-08:00","solved":false},"User:user:7":{"__typename":"User","uid":7,"login":"LiefZimmerman","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://community.f5.com/t5/s/zihoc95639/images/dS03LTVnQ0JFWg?image-coordinates=0%2C0%2C200%2C200"},"id":"user:7"},"TkbTopicMessage:message:289008":{"__typename":"TkbTopicMessage","subject":"External Monitor Information and Templates","conversation":{"__ref":"Conversation:conversation:289008"},"id":"message:289008","revisionNum":2,"uid":289008,"depth":0,"board":{"__ref":"Tkb:board:TechnicalArticles"},"author":{"__ref":"User:user:7"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":"","introduction":"","metrics":{"__typename":"MessageMetrics","views":4377},"postTime":"2020-05-19T12:59:10.000-07:00","lastPublishTime":"2025-01-27T10:16:42.517-08:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" This article is written by, and published on behalf of, DevCentral MVP Leonardo Souza. \n --- \n Introduction \n External monitors have been in use on F5 devices for a long time, and you can find a lot of code and articles about them here on DevCentral. However, most of those articles and code samples are very old and outdated. So, I think is a good time to update that with new information and new templates. In this article, I will provide some information about external monitors and templates you can use to build your own external monitor script and how you setup an external monitor. \n External Monitor \n The external monitor is a script that the F5 device will run to determine the status of a pool member (note: you can’t assign an external monitor to a node). The script runs the code and indicates if it was successful or not. The script indicates success outputting something to the standard output, and failure if nothing was outputted. The external monitor is your last resource in relation to monitoring, only to be used if there is no built-in monitoring that could be used to perform the monitoring. Built-in monitors are generally faster and easier to support as they do not require programming skills. External monitors run on Linux, while some built-in monitors can run on Linux or TMM. Linux is the kernel that CentOS uses, and CenOS is the base system that F5 uses, also known as the host system. TMM is the Kernel that F5 uses on TMOS, which is the F5 system. Running a built-in monitor in TMM is faster than in Linux. TMM monitors were introduced in version 13.1.0, for more information read this solution: https://support.f5.com/csp/article/K11323537 \n Configuration Steps \n Note: Instructions are for version 15.1.0, but should be similar in other versions, and you only need to setup this configuration in one device if you have an HA pair, as the configuration is shared during the config sync. Let’s create a simple external monitor to ping a pool member. You can do via tmsh, but it is simpler via GUI. Download the following file: https://raw.githubusercontent.com/leonardobdes/External_Monitor_Templates/master/bash_external_monitor_template.sh In the GUI go to System > File Management > External Monitor Program File List > Click Import. Select the file and give a name “simple_script” to the script, and import. That will import the file to the system, and make it available in the GUI. Now create a new monitor that points to the script file. In the GUI go to Local Traffic > Monitor > Click Create. Give the name “simple_monitor”, and select type as External. In the External Program select simple_script, click Finished. Now apply the new monitor to a pool member or pool. Here is the full configuration created: \n sys file external-monitor simple_script {\n checksum SHA1:817:01e135449c710e21dd090fdd688d940e319f97f5\n create-time 2020-04-23:17:45:25\n created-by admin\n last-update-time 2020-04-23:17:45:25\n mode 33261\n revision 1\n size 817\n source-path none\n updated-by admin\n}\nltm monitor external simple_monitor {\n defaults-from external\n interval 5\n run /Common/simple_script\n time-until-up 0\n timeout 16\n}\nltm pool pool_asm {\n members {\n LABServerPHP:http {\n address 172.16.0.14\n session monitor-enabled\n state up\n }\n test_server:http {\n address 172.16.0.19\n session monitor-enabled\n state down\n }\n }\n monitor simple_monitor\n}\n \n The first part is the script imported to the system. Next, the monitor that was created. Lastly, a pool using that monitor, also showing that one pool member is up and another one is down. \n Templates \n You can write the script to be used in the external monitor in any language that is supported by the F5 devices. I created a template for each of the following languages: \n \n Bash \n Perl \n Python \n Tcl \n \n For the templates go to this codeshare link: External Monitor Templates | DevCentral \n On my speed tests, I got these times with the templates: \n \n Bash - 0.014 seconds \n Perl - 0.020 seconds \n Tcl - 0.021 seconds \n Python - 0.084 seconds \n \n As you can see, Bash is definitely the fastest. Perl and Tcl are very close, and Python is the slowest. But, you may need to use something that works very well in Python, so it might be your only option. \n Bash Template \n I will explain the Bash template, but the logic is the same for other programming languages. Each script has 2 versions, one with debug and one without, as indicated in their filename. I will explain the one with debug. This tells the system what to use to run the script. \n #!/bin/bash \n The script will be called with 2 arguments, first is the IP, and second the port. The IP is passed in IPv6 format, example “::ffff:172.16.0.14”, so you need to remove “::ffff:” Old scripts, and also the sample_monitor script that comes in the system, use “sed 's/::ffff://'`” that are very slow compared with the above. \n ip=${1:7}\nport=$2 \n This will create a log line in the file /var/log/ltm. \n logger -p local0.notice \"$MON_TMPL_NAME - PID: $$ Name: $NODE_NAME IP: $ip Port: $port\" \n This is where you add your code to test the pool member. This example is just a ping against the IP, and the “-c1” means a single packet. The script then saves the result to know if it failed or not. Next, it will log another line to /var/log/ltm. \n ping -c1 $ip &> /dev/null\nresult=$?\nlogger -p local0.notice \"$MON_TMPL_NAME - PID: $$ Result: $result\" \n Lastly, the scripts check if the result was successful. If successful, it will send up to the standard output. \n [ $result -eq 0 ] && echo \"up\" \n The log lines will be similar to these ones. \n Apr 24 13:59:15 LABBIGIP1.lab.local notice logger[5178]: /Common/simple_monitor - PID: 5177 Name: /Common/test_server IP: 172.16.0.19 Port: 80\nApr 24 13:59:18 LABBIGIP1.lab.local notice logger[5201]: /Common/simple_monitor - PID: 5200 Name: /Common/LABServerPHP IP: 172.16.0.14 Port: 80\nApr 24 13:59:18 LABBIGIP1.lab.local notice logger[5203]: /Common/simple_monitor - PID: 5200 Result: 0\nApr 24 13:59:18 LABBIGIP1.lab.local notice logger[5209]: /Common/simple_monitor - PID: 5177 Result: 1 \n Note that the monitoring tests can run in parallel, so the lines will not be sequential. Use the PID number (Process ID), to match the lines. Old scripts, and also the sample_monitor script that comes in the system, have multiple lines of code to check if the same monitor is still running. If running, the new process kills the old one, before performing the monitoring. I tested this behavior in version 11.3.0 and 15.1.0, and this is not necessary. Before starting the new process for the script, the system will kill the old one, so any code in the script to control that is unnecessary as it will not be used. I assume this was necessary for old versions, as I never tested, but is not necessary for currently supported versions. \n Arguments and Variables \n The external monitor has multiple settings, including the ones you will find in any other monitor, like interval and timeout. However, it has 2 extra settings, those are arguments and variables. Arguments are passed when calling the script, for example: \n bash simple_script.sh IP port argument3 \n Variables are set as environment variables. You can then access that variable in the script. Be aware that the system will add the variables from the monitor first, and then add some extra variables. The environment variables for version 11.3.0 are, with values as an example: \n MON_TMPL_NAME=/Common/bash_external_monitor_template\nARGS_I=2 3 abc\nX=b\nPATH=/sbin:/bin:/usr/sbin:/usr/bin:/usr/contrib/bin:/usr/local/bin:/usr/contrib/sbin:/usr/local/sbin:/usr/libexec\nNODE_PORT=80\nPWD=/var/run\nSHLVL=1\nNODE_IP=::ffff:172.16.0.19\nNODE_NAME=/Common/test_server\nRUN_I=/Common/bash_external_monitor_template\n_=/bin/env \n ARGS_I will include the arguments you set in the monitor configuration, in this case, I have “2 3 abc”. X is the variable I added in the monitor configuration, with value “b”. That means you can use those variables in your script. Also, if you set the variable $PATH in your script as an example, it will get reset by the system, as the system variables are set after the monitor configuration variables. Let’s expand that with examples. Here is a monitor configuration with one argument and one variable. \n ltm monitor external simple_monitor {\n args 2\n defaults-from external\n interval 5\n run /Common/simple_script\n time-until-up 0\n timeout 16\n user-defined debug 1\n}\n \n Here is the part of the script that changed: \n ping -c${3} $ip &> /dev/null\nresult=$?\n[[ $debug -eq 1 ]] && logger -p local0.notice \"$MON_TMPL_NAME - PID: $$ Result: $result Ping: ${3}\"\n \n Now the script is using the argument 3 for the ping command, and the variable debug to define if log should occur. Let’s see the logs created: \n Apr 24 12:18:04 LABBIGIP1.lab.local notice logger[8179]: /Common/simple_monitor - PID: 8178 Name: /Common/test_server IP: 172.16.0.19 Port: 80\nApr 24 12:18:06 LABBIGIP1.lab.local notice logger[8196]: /Common/simple_monitor - PID: 8195 Name: /Common/LABServerPHP IP: 172.16.0.14 Port: 80\nApr 24 12:18:07 LABBIGIP1.lab.local notice logger[8206]: /Common/simple_monitor - PID: 8178 Result: 1 Ping: 2\nApr 24 12:18:07 LABBIGIP1.lab.local notice logger[8208]: /Common/simple_monitor - PID: 8195 Result: 0 Ping: 2 \n Ping is done with 2 packets, and the second log line still logged. \n Testing the Script \n External scripts are saved in this location: /config/filestore/files_d/Common_d/external_monitor_d That is for the Common partition, replace Common to the name of the partition if you import the script in another partition. The file will have an ID number (100479), and also a version number that changes when you update the file (19): :Common:simple_script_100479_19 That means you can go to that directory and run the script from there, and troubleshoot the errors. Don’t forget that when testing this way, you will not have the environment variables the system setup, you will have the environment variables from the shell where you run the command. Let’s test the simple simple_monitor discussed above. First, create the environment variables: \n export debug=1 MON_TMPL_NAME='/Common/simple_monitor' NODE_NAME='/Common/test_server' \n Run the script: \n ./\\:Common\\:simple_script_100479_22 '::ffff:172.16.0.14' 80 2 \n You will get the following log lines: \n Apr 24 16:03:48 LABBIGIP1.lab.local notice root[12206]: /Common/simple_monitor - PID: 12205 Name: /Common/test_server IP: 172.16.0.14 Port: 80\nApr 24 16:03:49 LABBIGIP1.lab.local notice root[12224]: /Common/simple_monitor - PID: 12205 Result: 0 Ping: 2 \n You need to escape the “:” with \\. After you do the tests, delete the environment variables created: \n unset debug MON_TMPL_NAME NODE_NAME\n\n \n \n Conclusion \n I hope this article provides you all the information you need to create your own external monitor. After you understand how it works, it is very simple to use. Just don’t forget, no “echo down”, only output something when the monitor was successful, and make sure you suppress any command output. 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BIG-IP and NetApp StorageGRID - Providing Fast and Scalable S3 API for AI apps","conversation":{"__ref":"Conversation:conversation:334320"},"id":"message:334320","revisionNum":7,"uid":334320,"depth":0,"board":{"__ref":"Tkb:board:TechnicalArticles"},"author":{"__ref":"User:user:405306"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":"","introduction":"","metrics":{"__typename":"MessageMetrics","views":770},"postTime":"2024-09-16T05:00:00.030-07:00","lastPublishTime":"2025-01-21T10:16:54.687-08:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" F5 BIG-IP, an industry-leading ADC solution, can provide load balancing services for HTTPS servers, with full security applied in-flight and performance levels to meet any enterprise’s capacity targets. Specific to S3 API, the object storage and retrieval protocol that rides upon HTTPS, an aligned partnering solution exists from NetApp, which allows a large-scale set of S3 API targets to ingest and provide objects. Automatic backend synchronization allows any node to be offered up at a target by a server load balancer like BIG-IP. This allows overall storage node utilization to be optimized across the node set, and scaled performance to reach the highest S3 API bandwidth levels, all while offering high availability to S3 API consumers. \n S3 compatible storage is becoming popular for AI applications due to its superior performance over traditional protocols such as NFS or CIFS, as well as enabling repatriation of data from the cloud to on-prem. These are scenarios where the amount of data faced is large, this drives the requirement for new levels of scalability and performance; S3 compatible object storages such as NetApp StorageGRID are purpose-built to reach such levels. \n Sample BIG-IP and StorageGRID Configuration \n This document is based upon tests and measurements using the following lab configuration. All devices in the lab were virtual machine-based offerings. \n \n The S3 service to be projected to the outside world, depicted in the above diagram and delivered to the client via the external network, will use a BIG-IP virtual server (VS) which is tied to an origin pool of three large-capacity StorageGRID nodes. The BIG-IP maintains the integrity of the NetApp nodes by frequent HTTP-based health checks. Should an unhealthy node be detected, it will be dropped from the list of active pool members. When content is written via the S3 protocol to any node in the pool, the other members are synchronized to serve up content should they be selected by BIG-IP for future read requests. \n The key recommendations and observations in building the lab include: \n \n Setup a local certificate authority such that all nodes can be trusted by the BIG-IP. Typically the local CA-signed certificate will incorporate every node’s FQDN and IP address within the listed subject alternate names (SAN) to make the backend solution streamlined with one single certificate. \n Different F5 profiles, such as FastL4 or FastHTTP, can be selected to reach the right tradeoff between the absolute capacity of stateful traffic load-balanced versus rich layer 7 functions like iRules or authentication. \n Modern techniques such as multi-part uploads or using HTTP Ranges for downloads can take large objects, and concurrently move smaller pieces across the load balancer, lowering total transaction times, and spreading work over more CPU cores. \n \n The S3 protocol, at its core, is a set of REST API calls. To facilitate testing, the widely used S3Browser (www.s3browser.com) was used to quickly and intuitively create S3 buckets on the NetApp offering and send/retrieve objects (files) through the BIG-IP load balancer. \n Setup the BIG-IP and StorageGrid Systems \n The StorageGrid solution is an array of storage nodes, provisioned with the help of an administrative host, the “Grid Manager”. For interactive users, no thick client is required as on-board web services allow a streamlined experience all through an Internet browser. The following is an example of Grid Manager, taken from a Chrome browser; one sees the three Storage Nodes setup have been successfully added. \n \n The load balancer, in our case, the BIG-IP, is set up with a virtual server to support HTTPS traffic and distributed that traffic, which is S3 object storage traffic, to the three StorageGRID nodes. The following screenshot demonstrates that the BIG-IP is setup in a standard HA (active-passive pair) configuration and the three pool members are healthy (green, health checks are fine) and receiving/sending S3 traffic, as the byte counts are seen in the image to be non-zero. On the internal side of the BIG-IP, TCP port 18082 is being used for S3 traffic. \n \n To do testing of the solution, including features such as multi-part uploads and downloads, a popular S3 tool, S3Browser, was downloaded and used. The following shows the entirety of the S3Browser setup. Simply create an account (StorageGRID-Account-01 in our example) and point the REST API endpoint at the BIG-IP Virtual Server that is acting as the secure front door for our pool of NetApp nodes. \n \n The S3 Access Key ID and Secret values are generated at turn-up time of the NetApp appliances. All S3 traffic will, of course, be SSL/TLS encrypted. BIG-IP will intercept the SSL traffic (high-speed decrypt) and then re-encrypt when proxying the traffic to a selected origin pool member. Other valid load balancer setups exist; one might include an “off load” approach to SSL, whereby the S3 nodes safely co-located in a data center may prefer to receive non-SSL HTTP S3 traffic. This may see an overall performance improvement in terms of peak bandwidth per storage node, but this comes at the tradeoff of security considerations. \n Experimenting with S3 Protocol and Load Balancing \n With all the elements in place to start understanding the behavior of S3 and spreading traffic across NetApp nodes, a quick test involved creating a S3 bucket and placing some objects in that new bucket. Buckets are logical collections of objects, conceptually not that different from folders or directories in file systems. In fact, a S3 bucket could even be mounted as a folder in an operating system such as Linux. In their simplest form, most commonly, buckets can simply serve as high-capacity, performant storage and retrieval targets for similarly themed structured or unstructured data. \n In the first test, we created a new bucket (“audio-clip-bucket”) and uploaded four sample files to the new bucket using S3Browser. \n \n We then zeroed the statistics for each pool member on the BIG-IP, to see if even this small upload would spread S3 traffic across more than a single NetApp device. \n \n Immediately after the upload, the counters reflect that two StorageGRID nodes were selected to receive S3 transactions. \n \n Richly detailed, per-transaction visibility can be obtained by leveraging the F5 SSL Orchestrator (SSLO) feature on the BIG-IP, whereby copies of the bi-directional S3 traffic decrypted within the load balancer can be sent to packet loggers, analytics tools, or even protocol analyzers like Wireshark. The BIG-IP also has an onboard analytics tool, Application Visibility and Reporting (AVR) which can provide some details on the nuances of the S3 traffic being proxied. AVR demonstrates the following characteristics of the above traffic, a simple bucket creation and upload of 4 objects. \n \n With AVR, one can see the URL values used by S3, which include the bucket name itself as well as transactions incorporating the object names as URLs. Also, the HTTP methods used included both GETS and PUTS. The use of HTTP PUT is expected when creating a new bucket. S3 is not governed by a typical standards body document, such as an IETF Request for Comment (RFC), but rather has evolved out of AWS and their use of S3 since 2006. \n For details around S3 API characteristics and nomenclature, this site can be referenced. For example, the expected syntax for creating a bucket is provided, including the fact that it should be an HTTP PUT to the root (/) URL target, with the bucket configuration parameters including name provided within the HTTP transaction body. \n \n \n Achieving High Performance S3 with BIG-IP and StorageGRID \n A common concern with protocols, such as HTTP, is head-of-line blocking, where one large, lengthy transaction blocks subsequent desired, queued transactions. This is one of the reasons for parallelism in HTTP, where loading 30 or more objects to paint a web page will often utilize two, four, or even more concurrent TCP sessions. Another performance issue when dealing with very large transactions is, without parallelism, even those most performant networks will see an established TCP session reach a maximum congestion window (CWND) where no more segments may be in flight until new TCP ACKs arrive back. Advanced TCP options like TCP exponential windowing or TCP SACK can help, but regardless of this, the achievable bandwidth of any one TCP session is bounded and may also frequently task only one core in multi-core CPUs. With the BIG-IP serving as the intermediary, large S3 transactions may default to “multi-part” uploads and downloads. The larger objects become a series of smaller objects that conveniently can be load-balanced by BIG-IP across the entire cluster of NetApp nodes. As displayed in the following diagram, we are asking for multi-part uploads to kick in for objects larger than 5 megabytes. \n \n After uploading a 20-megabyte file (technically, 20,000,000 bytes) the BIG-IP shows the traffic distributed across multiple NetApp nodes to the tune of 160.9 million bits. \n \n The incoming bits, incoming from the perspective of the origin pool members, confirm the delivery of the object with a small amount of protocol overhead (bits divided by eight to reach bytes). \n \n The value of load balancing manageable chunks of very large objects will pay dividends over time with faster overall transaction completion times due to the spreading of traffic across NetApp nodes, more TCP sessions reaching high congestion window values, and no single-core bottle necks in multicore equipment. \n Tuning BIG-IP for High Performance S3 Service Delivery \n The F5 BIG-IP offers a set of different profiles it can run its Local Traffic Manager (LTM) module in accordance with; LTM is the heart of the server load balancing function. The most performant profile in terms of attainable traffic load is the “FastL4” profile. This, and other profiles such as “OneConnect” or “FastHTTP”, can be tied to a virtual server, and details around each profile can be found here within the BIG-IP GUI: \n \n The FastL4 profile can increase virtual server performance and throughput for supported platforms by using the embedded Packet Velocity Acceleration (ePVA) chip to accelerate traffic. The ePVA chip is a hardware acceleration field programmable gate array (FPGA) that delivers high-performance L4 throughput by offloading traffic processing to the hardware acceleration chip. The BIG-IP makes flow acceleration decisions in software and then offloads eligible flows to the ePVA chip for that acceleration. For platforms that do not contain the ePVA chip, the system performs acceleration actions in software. \n Software-only solutions can increase performance in direct relationship to the hardware offered by the underlying host. As examples of BIG-IP virtual edition (VE) software running on mid-grade hardware platforms, results with Dell can be found here and similar experiences with HPE Proliant platforms are here. \n One thing to note about FastL4 as the profile to underpin a performance mode BIG-IP virtual server is that it is layer 4 oriented. For certain features that involve layer 7 HTTP related fields, such as using iRules to swap HTTP headers or perform HTTP authentication, a different profile might be more suitable. \n A bonus of FastL4 are some interesting specific performance features catering to it. In the BIG-IP version 17 release train, there is a feature to quickly tear down, with no delay, TCP sessions no longer required. Most TCP stacks implement TCP “2MSL” rules, where upon receiving and sending TCP FIN messages, the socket enters a lengthy TCP “TIME_WAIT” state, often minutes long. This stems back to historically bad packet loss environments of the very early Internet. A concern was high latency and packet loss might see incoming packets arrive at a target very late, and the TCP state machine would be confused if no record of the socket still existed. As such, the lengthy TIME_WAIT period was adopted even though this is consuming on-board resources to maintain the state. \n With FastL4, the “fast” close with TCP reset option now exists, such that any incoming TCP FIN message observed by BIG-IP will result in TCP RESETS being sent to both endpoints, normally bypassing TIME_WAIT penalties. \n \n OneConnect and FastHTTP Profiles \n As mentioned, other traffic profiles on BIG-IP are directed towards Layer 7 and HTTP features. One interesting profile is F5’s “OneConnect”. The OneConnect feature set works with HTTP Keep-Alives, which allows the BIG-IP system to minimize the number of server-side TCP connections by making existing connections available for reuse by other clients. This reduces, among other things, excessive TCP 3-way handshakes (Syn, Syn-Ack, Ack) and mitigates the small TCP congestion windows that new TCP sessions start with and only increases with successful traffic delivery. Persistent server-side TCP connections ameliorate this. \n \n When a new connection is initiated to the virtual server, if an existing server-side flow to the pool member is idle, the BIG-IP system applies the OneConnect source mask to the IP address in the request to determine whether it is eligible to reuse the existing idle connection. If it is eligible, the BIG-IP system marks the connection as non-idle and sends a client request over it. If the request is not eligible for reuse, or an idle server-side flow is not found, the BIG-IP system creates a new server-side TCP connection and sends client requests over it. \n The last profile considered is the “Fast HTTP” profile. The Fast HTTP profile is designed to speed up certain types of HTTP connections and again strives to reduce the number of connections opened to the back-end HTTP servers. This is accomplished by combining features from the TCP, HTTP, and OneConnect profiles into a single profile that is optimized for network performance. \n A resulting high performance HTTP virtual server processes connections on a packet-by-packet basis and buffers only enough data to parse packet headers. The performance HTTP virtual server TCP behavior operates as follows: the BIG-IP system establishes server-side flows by opening TCP connections to pool members. When a client makes a connection to the performance HTTP virtual server, if an existing server-side flow to the pool member is idle, the BIG-IP LTM system marks the connection as non-idle and sends a client request over the connection. \n Summary \n The NetApp StorageGRID multi-node S3 compatible object storage solution fits well with a high-performance server load balancer, thus making the F5 BIG-IP a good fit. S3 protocol can itself be adjusted to improve transaction response times, such as through the use of multi-part uploads and downloads, amplifying the default load balancing to now spread even more traffic chunks over many NetApp nodes. \n BIG-IP has numerous approaches to configuring virtual servers, from highest performance L4-focused profiles to similar offerings that retain L7 HTTP awareness. Lab testing was accomplished using the S3Browser utility and results of traffic flows were confirmed with both the standard BIG-IP GUI and the additional AVR analytics module, which provides additional protocol insight. 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Fully Qualified Domain Name (FQDN) for GSLB Pool Member with F5 DNS","conversation":{"__ref":"Conversation:conversation:332000"},"id":"message:332000","revisionNum":10,"uid":332000,"depth":0,"board":{"__ref":"Tkb:board:TechnicalArticles"},"author":{"__ref":"User:user:74420"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":"","introduction":"","metrics":{"__typename":"MessageMetrics","views":483},"postTime":"2024-10-04T05:00:00.027-07:00","lastPublishTime":"2024-10-04T05:00:00.027-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Normally, we define a specific IP (and port) to be used as GSLB pool member. This article provides a custom configuration to be able to use Fully Qualified Domain Name (FQDN) as GSLB pool member--with all GSLB features like health-check monitoring, load balancing method, persistence, etc. \n Despite GSLB as a mechanism to distribute traffic across datacenters having reached years of age, it has not become less relevant this recent years. The fact that internet infrastructure still rely heavily on DNS technology means GSLB is continuously used due to is lightweight nature and smooth integration. \n When using F5 DNS as GSLB solution, usually we are dealing with LTM and its VS as GSLB server and pool member respectively. Sometimes, we will add a non-LTM node as a generic server to provide inter-DC load balancing capability. Either way, we will end up with a pair of IP and port to represent the application, in which we sent a health-check against. \n Due to the trend of public cloud and CDN, there is a need to use FQDN as GSLB pool member (instead of IP and port pair). Some of us may immediately think of using a CNAME-type GSLB pool to accommodate this. However, there is a limitation in which BIG-IP requires a CNAME-type GSLB pool to use a wideIP-type pool member, in which we will end up with an IP and port pair (again!) \n \n We can use \"static target\", but there is \"side-effect\" where the pool member will always consider available (which then triggers the question why we need to use GSLB in the first place!). \n Additionally, F5 BIG-IP TMUI accepts FQDN input when we configure GSLB server and pool member. However, it will immediately translate to IP based on configured DNS. Thus, this is not the solution we are looking for \n \n Now this is where F5’s BIG-IP power (a.k.a programmability) comes into play. \n \n Enter the realm of customization... \n We all love customization, but at the same time do not want that to be overly complicated so that life becomes harder on day-2 🙃. Thus, the key is to use some customization, but simple enough to avoid unnecessary complication. Here is one idea to solve our FQDN as GSLB pool problem above \n \n The customized configuration object includes \n 1. External health-check monitor: \n \n Dynamically resolve DNS to translate FQDN into IP address \n Perform health-check monitoring against current IP address \n Result is used to determine GSLB pool member availability status \n \n 2. DNS iRules: \n \n Check #1: Checks if GSLB pool attached to wideIP contains only FQDN-type member (e.g. other pool referring to LTM VS is also attached to the wideIP)\n \n If false, do nothing (let DNS response refer to LTM VS) \n Otherwise, perform check #2 \n \n \n Check #2: Checks current health-check status of requested domain name\n \n If FQDN is up, modify DNS response to return current IP of FQDN \n Otherwise, perform fallback action as requirement (e.g. return empty response, return static IP, use fallback pool, etc.) \n \n \n \n 3. Internal Datagroup: \n \n Store current IP of FQDN, updated according to health-check interval \n Datagroup record value contains current IP if health-check success. Otherwise, the value contains empty data \n \n Here are some of the codes, where configured; wideIP is gslb.test.com, while GSLB pool member FQDN is arcadia.f5poc.id \n 1. External health-check monitor config \n gtm monitor external gslb_external_monitor { \n defaults-from external \n destination *:* interval 10 \n probe-timeout 5 \n run /Common/gslb_external_monitor_script \n timeout 120 \n #define FQDN here \n user-defined fqdn arcadia.f5poc.id \n} \n External health-check monitor script \n #!/bin/sh\npidfile=\"/var/run/$MONITOR_NAME.$1..$2.pid\"\nif [ -f $pidfile ]\nthen\n kill -9 -`cat $pidfile` > /dev/null 2>&1\nfi\necho \"$$\" > $pidfile\n# Obtain current IP for the FQDN\nresolv=`dig +short ${fqdn}`\n# The actual monitoring action here\ncurl -fIs -k https://${fqdn}/ --resolve ${fqdn}:443:${resolv} | grep -i HTTP 2>&1 > /dev/null\nstatus=$?\nif [ $status -eq 0 ]\nthen\n# Actions when health-check success \n rm -f $pidfile\n tmsh modify ltm data-group internal fqdn { records replace-all-with { $fqdn { data $resolv } } }\n echo \"sending monitor to ${fqdn} ${resolv} with result OK\" | logger -p local0.info\n echo \"up\"\nelse\n# Actions when health-check fails \n tmsh modify ltm data-group internal fqdn { records replace-all-with { $fqdn { } } }\n echo \"sending monitor to ${fqdn} ${resolv} with result NOK\" | logger -p local0.info\nfi\nrm -f $pidfile\n \n 2. DNS iRules \n when DNS_REQUEST { \n set qname [DNS::question name] \n # Obtain current IP for the FQDN\n set currentip [class match -value $qname equals fqdn] \n} \nwhen DNS_RESPONSE { \n set rname [getfield [lindex [split [DNS::answer]] 4] \"\\}\" 1 ]\n #Check if return is IP address of specially encoded FQDN IP, 10.10.10.10 in this example \n if {$rname eq \"10.10.10.10\" }{ \n #Response is only from pool with external monitor, meaning no other pool is attached to wideIP \n if {$currentip ne \"\"}{ \n #Current FQDN health-check success \n DNS::answer clear \n # Use current IP to construct DNS answer section\n DNS::answer insert \"[DNS::question name]. 123 [DNS::question class] [DNS::question type] $currentip\" \n } else { \n #Current FQDN health-check failed #Define action to be performed here \n DNS::answer clear \n } \n } \n}\n \n 3. Internal Datagroup \n ltm data-group internal fqdn {\n records {\n # Define FQDN as record name\n arcadia.f5poc.id {\n # Record data contains IP, where this will be continuously updated by external monitoring script\n data 158.140.176.219\n }\n }\n type string\n}\n \n *GSLB virtual server configuration \n \n \n Some testing \n \n The resolve will follow whichever current IP address for the FQDN. If a returning CNAME response is required, you can do so by modifying DNS irules above. \n The logic and code are open to any improvement, so leave your suggestions in the comments if you have any. Thanks! 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