Introducing Secure MCN features on F5 Distributed Cloud
Introduction F5 Distributed Cloud Services offers many secure multi-cloud networking features. In the video linked below, I demonstrate how to connect a Secure Mesh Customer Edge (CE) Site running on VMware and using common hardware. This on-prem CE is joined to a site mesh group of three other CE's, two of which are run on the public cloud providers AWS and Azure. Secure Mesh CE is a newly enhanced feature in Distributed Cloud that allows CE's not running in public cloud providers to run on hardware with unique and different configurations. Specifically, it's now possible to deploy site mesh transit networking to all CE's having one, two, or more NIC's, with each CE having its own unique physical configuration for networking. See my article on Secure Mesh Site Networking to learn how to set up and configure secure mesh sites. In addition to secure mesh networking, on-prem CE's can be deployed without app management features, giving organizations the flexibility to conserve deployed resources. Organizations can now choose whether to deploy AppStack CE's, where the CE's can manage and run K8s compute workloads deployed at the site, or use networking-focused CE's freeing up resources that would otherwise be used managing the apps. Whether deploying an AppStack or Secure Mesh CE, both types support Distributed Cloud's comprehensive set of security features, including DDoS, WAF, API protection, Bot, and Risk management. Secure MCN deployment capabilities include the following capabilities: Secure Multi-Cloud Network Fabric (secure connectivity) Discover any app running anywhere across your environments Cloud/On-Prem Customer Edge (CE) Private link connectivity orchestration with F5 XC as-a-service using any transport provider ➡️ Example: AWS PrivateLink, Azure CloudLink, Private transport (IP, MPLS, etc) L3 Network Connect & L7 App Connect capabilities L3/L4 DDoS + Enhanced intent-based firewall policies Security Service insertion w/ support for BIG-IP and Palo Alto Firewalls Application Security Services - WAF, API Protection, L7 DoS, Bot Defense, Client-side defense and more SaaS and Automation for Security, Network, & Edge Compute Powerful monitoring dashboards & troubleshooting tools for the entire secure multi-cloud network fabric Gain visibility into how and which API's are being consumed in workflows ➡️ Monitor and troubleshoot apps including their API's In the following video, I introduce the components that make up a Secure MCN deployment, and then walk through configuring the security features and show how to observe app performance and remediate security related incidents. 0-3:32 - Overview of Secure MCN features 3:32-9:20 - Product Demo Resources Distributed Cloud App Delivery Fabric Workflow Guide (GitHub) Secure MCN Article Series Secure MCN Intro: Introducing Secure MCN features on F5 Distributed Cloud Secure MCN Part 1: Using Distributed Application Security Policies in Secure Multicloud Networking Customer Edge Sites Secure MCN Part 2: The App Delivery Fabric with Secure Multicloud Networking Secure MCN Part 3: The Secure Network Fabric with Multicloud Network Segmentation & Private Provider Network Connectivity Related Technical Articles 🔥 ➡️ Combining the key aspects of Secure MCN with GenAI apps: Protect multi-cloud and Edge Generative AI applications with F5 Distributed Cloud Scale Your DMZ with F5 Distributed Cloud Services Driving Down Cost & Complexity: App Migration in the Cloud How To Secure Multi-Cloud Networking with Routing & Web Application and API Protection Secure Mesh Site Networking (DevCentral) A Complete Multi-Cloud Networking Walkthrough (DevCentral) Product Documentation How-To Create Secure Mesh Sites Product Information Distributed Cloud Network Connect Distributed Cloud App ConnectHow To Secure Multi-Cloud Networking with Routing & Web Application and API Protection
Introduction With the proliferation of intra-cloud networking requirements, continuing, organizations are increasingly leveraging multi-cloud solutions to optimize performance, cost, and resilience. However, with this approach comes the challenge of ensuring robust security across diverse cloud and on-prem environments. Secure multi-cloud networking with advanced web application and API protection services, is essential to safeguard digital assets, maintain compliance, and uphold operational integrity. Understanding Multi-Cloud Networking Multi-cloud networking involves orchestrating connectivity between multiple cloud platforms such as AWS, Microsoft Azure, Google Cloud Platform, and others including on-prem and private data centers. This approach allows organizations to avoid vendor lock-in, enhance redundancy, and tailor services to specific workloads. However, managing networking and web application security across these platforms can be complex due to different platform security models, configurations, and interfaces. Key Components of Multi-Cloud Networking Inter-cloud Connectivity: Establishing secure connections between multiple cloud providers to ensure seamless data flow and application interoperability. Unified Management: Implementing centralized management tools to oversee network configurations, policies, and security protocols across all cloud environments. Automated Orchestration: Utilizing automation to provision, configure, and manage network resources dynamically, reducing manual intervention and potential errors. Compliance and Governance: Ensuring adherence to regulatory requirements and best practices for data protection and privacy across all cloud platforms. Securing Multi-Cloud Environments Security is paramount in multi-cloud networking. With multiple entry points and varying security measures across different cloud providers, organizations must adopt a comprehensive strategy to protect their assets. Strategies for Secure Multi-Cloud Networking Zero Trust Architecture: Implementing a zero-trust model that continuously verifies and validates every request, irrespective of its source, to mitigate risks. Encryption: Utilizing advanced encryption methods for data in transit and at rest to protect against unauthorized access. Continuous Monitoring: Deploying monitoring tools to detect, analyze, and respond to threats in real-time. Application Security: Using a common framework for web application and API security reduces the number of steps needed to identify and remediate security risks and misconfigurations across disparate infrastructures. Web Application and API Protection Services Web applications and APIs are critical components of modern digital ecosystems. Protecting these assets from cyber threats is crucial, especially in a multi-cloud environment where they may be distributed across various platforms. Comprehensive Web Application Protection Web Application Firewalls (WAFs) play a vital role in safeguarding web applications. They filter and monitor HTTP traffic between a web application and the internet, blocking malicious requests and safeguarding against common threats such as SQL injection, cross-site scripting (XSS), and DDoS attacks. Advanced Threat Detection: Employing machine learning and artificial intelligence to identify and block sophisticated attacks. Application Layer Defense: Providing protection at the application layer, where traditional network security measures may fall short. Scalability and Performance: Ensuring WAF solutions can scale and perform adequately in response to varying traffic loads and attack volumes. Securing APIs in Multi-Cloud Environments APIs are pivotal for integration and communication between services. Securing APIs involves protecting them from unauthorized access, misuse, and exploitation. Authentication and Authorization: Implement strong authentication mechanisms such as OAuth and JWT to ensure only authorized users and applications can access APIs. Rate Limiting: Controlling the number of API calls to prevent abuse and ensure fair usage across consumers. Input Validation: Validating input data to prevent injection attacks and ensure data integrity. Threat Detection: Monitoring API traffic for anomalies and potential threats, and responding swiftly to mitigate risks. Best Practices for Secure Multi-Cloud Networking To effectively manage and secure multi-cloud networks, organizations should adhere to best practices that align with their operational and security objectives. Adopt a Holistic Security Framework A holistic security framework encompasses the entire multi-cloud environment, focusing on integration and coordination between different security measures across cloud platforms. Unified Policy Enforcement: Implementing consistent security policies across all cloud environments to ensure uniform protection. Regular Audits: Conducting frequent security audits to identify vulnerabilities, assess compliance, and improve security postures. Incident Response Planning: Developing and regularly updating incident response plans to handle potential breaches and disruptions efficiently. Leverage Security Automation Automation can significantly enhance security in multi-cloud environments by reducing human errors and ensuring timely responses to threats. Automated Compliance Checks: Using automation to continuously monitor and enforce compliance with security standards and regulations. Real-time Threat Mitigation: Implementing automated remediation processes to address security threats as they are detected. Demo: Bringing it Together with F5 Distributed Cloud Using services in Distributed Cloud, F5 brings everything together. Deploying and orchestrating connectivity between hybrid and multi-cloud environments, Distributed Cloud not only connects these environments, it also secures them with universal Web App and API Protection policies. The following solution uses Distributed Cloud Network Connect, App Connect, and Web App & API Protection to connect, deliver, and secure an application with services that exist in different cloud and on-prem environments. This video shows how each of the features in this solution come together: Bringing it together with Automation Orchestrating this end-to-end, including the Distributed Cloud components itself is trivial using the combination of GitHub Workflow Actions and Terraform. The following automation workflow guide and companion article at DevCentral provides all the steps and modular code necessary to build a complete multicloud environment, manage Kubernetes clusters, and deploy the sample functional multi-site application, Arcadia Finance. GitHub Repository: https://github.com/f5devcentral/f5-xc-terraform-examples/tree/main/workflow-guides/smcn/mcn-distributed-apps-l3 Conclusion Secure multi-cloud networking, combined with robust web application and API protection services, is vital for organizations seeking to leverage the benefits of a multi-cloud strategy without compromising security. By adopting comprehensive security measures, enforcing best practices, and leveraging advanced technologies, organizations can safeguard their digital assets, ensure compliance, and maintain operational integrity in a dynamic and ever-evolving cloud landscape. Additional Resources Introducing Secure MCN features on F5 Distributed Cloud Driving Down Cost & Complexity: App Migration in the Cloud The App Delivery Fabric with Secure Multicloud Networking Scale Your DMZ with F5 Distributed Cloud Services Seamless Application Migration to OpenShift Virtualization with F5 Distributed Cloud Automate Multicloud Networking w/ Terraform: routing and app connect on F5 Distributed Cloud
F5 Distributed Cloud – CE High Availability Options: A Comparative Exploration
This article explores an alternative approach to achieve HA across single CE nodes, catering for use cases requiring higher performance and granular control over redundancy and failover management. Introduction F5 Distributed Cloud offers different techniques to achieve High Availability (HA) for Customer Edge (CE) nodes in an active-active configuration to provide redundancy, scaling on-demand and simplify management. By default, F5 Distributed Cloud uses a method for clustering CE nodes, in which CEs keep track of peers by sending heartbeats and facilitating traffic exchange among themselves. This method also handles the automatic transfer of traffic, virtual IPs, and services between CE peers —excellent for simplified deployment and running App Stack sites hosting Kubernetes workloads. However, if CE nodes are deployed mainly to manage L3/L7 traffic and application security, this default model might lack the flexibility needed for certain scenarios. Many of our customers tell us that achieving high availability is not so straightforward with the current clustering model. These customers often have a lot of experience in managing redundancy and high availability across traditional network devices. They like to manage everything themselves—from scheduling when to switch over to a redundant pair (planned failover), to choosing how many network paths (tunnels) to use between CEs to REs (Regional Edges) or other CEs. They also want to handle any issues device by device, decide the number of CE nodes in a redundancy group, and be able to direct traffic to different CEs when one is being updated. Their feedback inspired us to write this article, where we explore a different approach to achieve high availability across CEs. The default clustering model is explained in this document: https://docs.cloud.f5.com/docs/ves-concepts/site#cluster-of-nodes Throughout this article, we will dive into several key areas: An overview of the default CE clustering model, highlighting its inherent challenges and advantages. Introduction to an alternative clustering strategy: Single Node Clustering, including: An analysis of its challenges and benefits. Identification of scenarios where this approach is most applicable. A guide to the configuration steps necessary to implement this model. An exploration of failover behavior within this framework. A comparison table showing how this new method differs from the default clustering method. By the end of this article, readers will gain an understanding of both clustering approaches, enabling informed decisions on the optimal strategy for their specific needs. Default CE Clustering Overview In a standard CE clustering setup, a cluster must have at least three Master nodes, with subsequent additions acting as Worker nodes. A CE cluster is configured as a "Site," centralizing operations like pool configuration and software upgrades to simplify management. In this clustering method, frequent communication is required between control plane components of the nodes on a low latency network. When a failover happens, the VIPs and services - including customer’s compute workloads - will transition to the other active nodes. As shown in the picture above, a CE cluster is treated as a single site, regardless of the number of nodes it contains. In a Mesh Group scenario, each mesh link is associated with one single tunnel connected to the cluster. These tunnels are distributed among the master nodes in the cluster, optimizing the total number of tunnels required for a large-scale Mesh Group. It also means that the site will be connected to REs only via 2 tunnels – one to each RE. Design Considerations for Default CE Clustering model: Best suited for: 1- App Stack Sites: Running Kubernetes workloads necessitates the default clustering method for container orchestration across nodes. 2- Large-scale Site-Mesh Groups (SMG) 3- Cluster-wide upgrade preference: Customers who favour managing nodes collectively will find cluster-wide upgrades more convenient, however without control over the upgrade sequence of individual nodes. Challenges: o Network Bottleneck for Ingress Traffic: A cluster connected to two Regional Edge (RE) sites via only 2 tunnels can lead to only two nodes processing external (ingress) traffic, limiting the use of additional nodes to process internal traffic only. o Three-master node requirement: Some customers are accustomed to dual-node HA models and may find the requirement for three master nodes resource-intensive. o Hitless upgrades: Controlled, phased upgrades are preferred by some customers for testing before widespread deployment, which is challenging with cluster-wide upgrades. o Cross-site deployments: High network latency between remote data centers can impact cluster performance due to the latency sensitivity of etcd daemon, the backbone of cluster state management. If the network connection across the nodes gets disconnected, all nodes will most likely stop the operation due to the quorum requirements of etcd. Therefore, F5 recommends deploying separate clusters for different physical sites. o Service Fault Sprawl and limited Node fault tolerance: Default clusters can sometimes experience a cascading effect where a fault in a node spreads throughout the cluster. Additionally, a standard 3-node cluster can generally only tolerate the failure of two nodes. If the cluster was originally configured with three nodes, functionality may be lost if reduced to a single active node. These limitations stem from the underlying clustering design and its dependency on etcd for maintaining cluster state. The Alternative Solution: HA Between Multiple Single Nodes The good news is that we can achieve the key objectives of the clustering – which are streamlined management and high availability - without the dependency on the control plane clustering mechanisms. Streamlined management using “Virtual Site”: F5 Distributed Cloud provides a mechanism called “Virtual Site” to perform operations on a group of sites (site = node or cluster of nodes), reducing the need to repeat the same set of operations for each site. The “Virtual Site” acts as an abstraction layer, grouping nodes tagged with a unique label and allows collectively addressing these nodes as a single entity. Configuration of origin pools and load balancers can reference Virtual Sites instead of individual sites/nodes, to facilitate cluster-like management for two or more nodes and enabling controlled day 2 operations. When a node is disassociated from Virtual Site by removing the label, it's no longer eligible for new connections, and its listeners are simultaneously deactivated. Upgrading nodes is streamlined: simply remove the node's label to exclude it from the Virtual Site, perform the upgrade, and then reapply the label once the node is operational again. This procedure offers you a controlled failover process, ensuring minimal disruption and enhanced manageability by minimizing the blast radius and limiting the cope of downtime. As traffic is rerouted to other CEs, if something goes wrong with an upgrade of a CE node, the services will not be impacted. HA/Redundancy across multiple nodes: Each single node in a Virtual Site connect to dual REs through IPSec or SSL/TLS tunnels, ensuring even load distribution and true active-active redundancy. External (Ingress) Traffic: In the Virtual Site model, the Regional Edges (REs) distribute external traffic evenly across all nodes. This contrasts with the default clustering approach where only two CE nodes are actively connected to the REs. The main Virtual Site advantage lies in its true active/active configuration for CEs, increasing the total ingress traffic capacity. If a node becomes unavailable, the REs will automatically reroute the new connections to another operational node within the Virtual Site, and the services (connection to origin pools) remain uninterrupted. Internal (East-West) Traffic: For managing internal traffic within a single CE node in a Virtual Site (for example, when LB objects are configured to be advertised within the local site), all network techniques applicable to the default clustering model can be employed in this model as well, except for the Layer 2 attachment (VRRP) method. Preferred load distribution method for internal traffic across CEs: Our preferred methods for load balancing across CE nodes are either DNS based load balancing or Equal-Cost Multi-Path (ECMP) routing utilizing BGP for redundancy. DNS Load Balancer Behavior: If a node is detached from a Virtual Site, its associated listeners and Virtual IPs (VIPs) are automatically withdrawn. Consequently, the DNS load balancer's health checks will mark those VIPs as down and prevent them from receiving internal network traffic. Current limitation for custom VIP and BGP: When using BGP, please note a current limitation that prevents configuring a custom VIP address on the Virtual Site. As a workaround, custom VIPs should be advertised on individual sites instead. The F5 product team is actively working to address this gap. For a detailed exploration of traffic routing options to CEs, please refer to the following article here: https://community.f5.com/kb/technicalarticles/f5-distributed-cloud---customer-edge-site---deployment--routing-options/319435 Design Considerations for Single Node HA Model: Best suited for: 1- Customers with high throughput requirement: This clustering model ensures that all Customer Edge (CE) nodes are engaged in managing ingress traffic from Regional Edges (REs), which allows for scalable expansion by adding additional CEs as required. In contrast, the default clustering model limits ingress traffic processing to only two CE nodes per cluster, and more precisely, to a single node from each RE, regardless of the number of worker nodes in the cluster. Consequently, this model is more advantageous for customers who have high throughput demands. 2- Customers who prefer to use controlled failover and software upgrades This clustering model enables a sequential upgrade process, where nodes are updated individually to ensure each node upgrades successfully before moving on to the other nodes. The process involves detaching the node from the cluster by removing its site label, which causes redirecting traffic to the remaining nodes during the upgrade. Once upgraded, the label is reapplied, and this process is repeated for each node in turn. This is a model that customers have known for 20+ years for upgrade procedures, with a little wrinkle with the label. 3- Customers who prefer to distribute the load across remote sites Nodes are deployed independently and do not require inter-node heartbeat communication, unlike the default clustering method. This independence allows for their deployment across various data centers and availability zones while being managed as a single entity. They are compatible with both Layer 2 (L2) spanned and Layer 3 (L3) spanned data centers, where nodes in different L3 networks utilize distinct gateways. As long as the nodes can access the origin pools, they can be integrated into the same "Virtual Site". This flexibility caters to customers' traditional preferences, such as deploying two CE nodes per location, which is fully supported by this clustering model. Challenges: Lack of VRRP Support: The primary limitation of this clustering method is the absence of VRRP support for internal VIPs. However, there are some alternative methods to distribute internal traffic across CE nodes. These include DNS based routing, BGP with Equal-Cost Multi-Path (ECMP) routing, or the implementation of CEs behind another Layer 4 (L4) load balancer capable of traffic distribution without source address alteration, such as F5 BIG-IPs or the standard load balancers provided by Azure or AWS. Limitation on Custom VIP IP Support: Currently, the F5 Distributed Cloud Console has a restriction preventing the configuration of custom virtual IPs for load balancer advertisements on Virtual Sites. We anticipate this limitation will be addressed in future updates to the F5 Distributed Cloud platform. As a temporary solution, you can advertise the LB across multiple individual sites within the Virtual Site. This approach enables the configuration of custom VIPs on those sites. Requires extra steps for upgrading nodes Unlike the Default clustering model where upgrades can be performed collectively on a group of nodes, this clustering model requires upgrading nodes on an individual basis. This may introduce more steps, especially in larger clusters, but it remains significantly simpler than traditional network device upgrades. Large-Scale Mesh Group: In F5 Distributed Cloud, the "Mesh Group" feature allows for direct connections between sites (whether individual CE sites or clusters of CEs) and other selected sites through IPSec tunnels. For CE clusters, tunnels are established on a per-cluster basis. However, for single-node sites, each node creates its own tunnels to connect with remote CEs. This setup can lead to an increased number of tunnels needed to establish the mesh. For example, in a network of 10 sites configured with dual-CE Virtual Sites, each CE is required to establish 18 IPSec tunnels to connect with other sites, or 19 for a full mesh configuration. Comparatively, a 10-site network using the default clustering method—with a minimum of 3 CEs per site—would only need up to 9 tunnels from each CE for full connectivity. Opting for Virtual Sites with dual CEs, a common choice, effectively doubles the number of required tunnels from each CE when compared to the default clustering setup. However, despite this increase in tunnels, opting for a Mesh configuration with single-node clusters can offer advantages in terms of performance and load distribution. Note: Use DC Groups as an alternative solution to Secure Mesh Group for CE connectivity: For customers with existing private connectivity between their CE nodes, running Site Mesh Group (SMG) with numerous IPsec tunnels can be less optimal. As a more scalable alternative for these customers, we recommend using DC Cluster Group (DCG). This method utilizes IP-in-IP tunnels over the existing private network, eliminating the need for individual encrypted IPsec tunnels between each node and streamlining communication between CE nodes via IP-n-IP encapsulations. Configuration Steps The configuration for creating single node clusters involves the following steps: Creating a Label Creating a Virtual Site Applying the label to the CE nodes (sites) Review and validate the configuration The detailed configuration guide for the above steps can be found here: https://docs.cloud.f5.com/docs/how-to/fleets-vsites/create-virtual-site Example Configuration: In this example, you can create a label called "my-vsite" to group CE nodes that belong to the same Virtual Site. Within this label, you can then define different values to represent different environments or clusters, such as specific Azure region or an on-premise data center. Then a Virtual Site of “CE” type can be created to represent the CE cluster in “Azure-AustraliaEast-vSite" and tied to any CE that is tagged with the label “my-vsite=Azure-AustraliaEast-vSite”: Now, any CE node that should join the cluster (Virtual Site), should get this label: Verification: To confirm the Virtual Site configuration is functioning as intended, we joined two CEs (k1-azure-ce2 and k1-azure-ce03) into the Virtual Site and evaluated the routing and load balancing behavior. Test 1: Public Load Balancer (Virtual Site referenced in the pool) The diagram shows a public "Load Balancer" advertised on the RE referencing a pool that uses the newly created Virtual Site to access the private application: As shown below, the pool member was configured to be accessed through the Virtual Site: Analysis of the request logs in the Performance dashboard confirmed that all requests to the public website were evenly distributed across both CEs. Test 2: Internal Load Balancer (LB advertised on the Virtual Site) We deployed an internal Load Balancer and advertised it on the newly created Virtual Site, utilizing the pool that also references the same Virtual Site (k1-azure-ce2 and k1-azure-ce03). As shown below, the Load Balancer was configured to be advertised on the Virtual Site. Note: Here we couldn't use a "shared" custom VIP across the Virtual Site due to a current platform constraint. If a custom VIP is required, we should use "site" as opposed to "Virtual Site" and advertise the Load Balancer on all sites, like below picture: Request logs revealed that when traffic reached either CE node within the Virtual Site, the request was processed and forwarded locally to the pool member. In the example below: src_site: Indicates the CE (k1-azure-ce2) that processed the request. src_ip: Represents the client's source IP address (192.168.1.68). dst_site: Indicates the CE (k1-azure-ce2) from which the pool member is accessed. dst_ip: Represents the IP address of the pool member (192.168.1.6). Resilience Testing: To assess the Virtual Site's resilience, we intentionally blocked network access from k1-azure-ce2 CE to the pool member (192.168.1.6). The CE automatically rerouted traffic to the pool member via the other CE (k1-azure-ce03) in the Virtual Site. Note: By default, CEs can communicate with each other via the F5 Global Network. This can be customized to use direct connectivity through tunnels if the CEs are members of the same DC Cluster Group (IP-n-IP tunneling) or Secure Mesh Groups (IPSec tunneling). The following picture shows the traffic flow via F5 Global Network. The following picture shows the traffic flow via the IP-n-IP tunnel when a DC Clustering Group (DCG) is configured across the CE nodes. Failover Behaviour When a CE node is tied to a Virtual Site, all internal Load Balancers (VIPs) advertised on that Virtual Site will be deployed in the CE. Additionally, the Regional Edge (RE) begins to use this node as one of the potential next hops for connections to the origin pool. Should the CE become unavailable, or if it lacks the necessary network access to the origin server, the RE will almost seamlessly reroute connections through the other operational CEs in the Virtual Site. Uncontrolled Failover: During instances of uncontrolled failover, such as when a node is unexpectedly shut down from the hypervisor, we have observed a handful of new connections experiencing timeouts. However, these issues were resolved by implementing health checks within the origin pool, which prevented any subsequent connection drops. Note: Irrespective of the clustering model in use, it's always recommended to configure health checks for the origin pool. This practice enhances failover responsiveness and mitigates any additional latency incurred during traffic rerouting. Controlled Failover: The moment a CE node is disassociated from the Virtual Site — by the removal of its label— the CE node will not be used by RE to connect to origin pools anymore. At the same time, all Load Balancer listeners associated with that Virtual Site are withdrawn from the node. This effectively halts traffic processing for those applications, preventing the node from receiving related traffic. During controlled failover scenarios, we have observed seamless service continuity on externally advertised services (to REs). On-Demand Scaling: F5 Distributed Cloud provides a flexible solution that enables customers to scale the number of active CE nodes according to demand. This allows you to easily add more powerful CE nodes during peak periods (such as promotional events) and then remove them when demand subsides. With the Virtual Sites method, you can even mix and match node sizes within your cluster (Virtual Site), providing granular control over resources. It's advisable to monitor CE node performance and implement node related alerts. These alerts notify you when nodes are operating at high capacity, allowing for timely addition of extra nodes as needed. Moreover, you can monitor node’s health in the dashboard. CPU, Memory and Disk utilizations of nodes can be a good factor in determining if more nodes are needed or not. Furthermore, the use of Virtual Sites makes managing this process even easier, thanks to labels. Node Based Alerts: Node-based alerts are essential for maintaining efficient CE operations. Accessing the alerts in the Console: To view alerts, go to Multi-Cloud Network Connect > Notifications > Alerts. Here, you can see both "Active Alerts" and "All Alerts." Alerts related to node health fall under the "infrastructure" alert group. The following screenshot shows alerts indicating high loads on the nodes. Configuring Alert Policies: Alert policies determine the notification process for raised alerts. To set up an alert policy, navigate to Multi-Cloud Network Connect > Alerts Management > Alert Policies. An alert policy consists of two main elements: the alert receiver configuration and the policy rules. Configuring Alert Receiver: The configuration allows for integration with platforms like Slack and PagerDuty, among others, facilitating notifications through commonly used channels. Configuring Alert Rules: For alert selection, we recommend configuring notifications for alerts with severity of “Major” or “Critical” at a minimum. Alternatively, the “infrastructure” group which includes node-based alerts can be selected. Comparison Table Criteria Default Cluster Single Node HA Minimum number of nodes in HA 3 2 Upgrade operations Per cluster Per Node Network redundancy and client side routing for east-west traffic VRRP, BGP, DNS, L4/7 LB DNS, L4/7 LB, BGP* Tunnels to RE 2 tunnels per cluster 2 tunnels per node Tunnels to other CEs (SMG or DCG) 1 tunnel from each cluster 1 tunnel from each node External traffic processing Limited to 2 nodes All nodes will be active Internal traffic processing All nodes can be active All nodes can be active Scale management in Public Cloud Sites Straightforward, by configuring ingress interfaces in Azure/AWS/GCP sites Straightforward, by adding or removing the labels Scale management in Secure Mesh Sites Requires reconfiguring the cluster (secure mesh site) - may cause interruption Straightforward, by adding or removing the labels Custom VIP IP Available Not Available (Planned to be available in future releases), workaround available. Node sizes All nodes should be same size. Upgrading node size in a cluster is a disruptive operation. Any node sizes or clusters can join the Virtual Site * When using BGP, please note a current limitation that prevents configuring custom VIP address on the Virtual Site. Conclusion: F5 Distributed Cloud offers a flexible approach to High Availability (HA) across CE nodes, allowing customers to select the redundancy model that best fits their specific use cases and requirements. While we continue to advocate for default clustering approach due to their operational simplicity and shared VRRP VIP or, unified network configuration benefits, especially for routine tasks like upgrades, the Virtual Site and single node HA model presents some great use cases. It not only addresses the limitations and challenges of the default clustering model, but also introduces a solution that is both scalable and adaptable. While Virtual Sites offer their own benefits, we recognize they also present trade-offs. The overall benefits, particularly for scenarios demanding high ingress (RE to CE) throughput and controlled failover capabilities cater to specific customer demands. The F5 product and development team remains committed to addressing the limitations of both default clustering and Virtual Sites discussed throughout this article. Their focus is on continuous improvement and finding the solutions that best serve our customers' needs. References and Additional Links: Default Clustering model: https://docs.cloud.f5.com/docs/ves-concepts/site#cluster-of-nodes Configuration guide for Virtual Sites: https://docs.cloud.f5.com/docs/how-to/fleets-vsites/create-virtual-site Routing Options for CEs: https://community.f5.com/kb/technicalarticles/f5-distributed-cloud---customer-edge-site---deployment--routing-options/319435 Configuration guide for DC Clustering Group: https://docs.cloud.f5.com/docs/how-to/advanced-networking/configure-dc-cluster-groupSecure AI RAG using F5 Distributed Cloud in Red Hat OpenShift AI and NetApp ONTAP Environment
Introduction Retrieval Augmented Generation (RAG) is a powerful technique that allows Large Language Models (LLMs) to access information beyond their training data. The “R” in RAG refers to the data retrieval process, where the system retrieves relevant information from an external knowledge base based on the input query. Next, the “A” in RAG represents the augmentation of context enrichment, as the system combines the retrieved relevant information and the input query to create a more comprehensive prompt for the LLM. Lastly, the “G” in RAG stands for response generation, where the LLM generates a response with a more contextually accurate output based on the augmented prompt as a result. RAG is becoming increasingly popular in enterprise AI applications due to its ability to provide more accurate and contextually relevant responses to a wide range of queries. However, deploying RAG can introduce complexity due to its components being located in different environments. For instance, the datastore or corpus, which is a collection of data, is typically on-premise for enhanced control over data access and management due to data security, governance, and compliance with regulations within the enterprise. Meanwhile, inference services are often deployed in the cloud for their scalability and cost-effectiveness. In this article, we will discuss how F5 Distributed Cloud can simplify the complexity and securely connect all RAG components seamlessly for enterprise RAG-enabled AI applications deployments. Specifically, we will focus on Network Connect, App Connect, and Web App & API Protection. We will demonstrate how these F5 Distributed Cloud features can be leveraged to secure RAG in collaboration with Red Hat OpenShift AI and NetApp ONTAP. Example Topology F5 Distributed Cloud Network Connect F5 Distributed Cloud Network Connect enables seamless and secure network connectivity across hybrid and multicloud environments. By deploying F5 Distributed Cloud Customer Edge (CE) at site, it allows us to easily establish encrypted site-to-site connectivity across on-premises, multi-cloud, and edge environment. Jensen Huang, CEO of NVIDIA, has said that "Nearly half of the files in the world are stored on-prem on NetApp.”. In our example, enterprise data stores are deployed on NetApp ONTAP in a data center in Seattle managed by organization B (Segment-B: s-gorman-production-segment), while RAG services, including embedding Large Language Model (LLM) and vector database, is deployed on-premise on a Red Hat OpenShift cluster in a data center in California managed by Organization A (Segment-A: jy-ocp). By leveraging F5 Distributed Cloud Network Connect, we can quickly and easily establish a secure connection for seamless and efficient data transfer from the enterprise data stores to RAG services between these two segments only: F5 Distributed Cloud CE can be deployed as a virtual machine (VM) or as a pod on a Red Hat OpenShift cluster. In California, we deploy the CE as a VM using Red Hat OpenShift Virtualization — click here to find out more on Deploying F5 Distributed Cloud Customer Edge in Red Hat OpenShift Virtualization: Segment-A: jy-ocp on CE in California and Segment-B: s-gorman-production-segment on CE in Seattle: Simply and securely connect Segment-A: jy-ocp and Segment-B: s-gorman-production-segment only, using Segment Connector: NetApp ONTAP in Seattle has a LUN named “tbd-RAG”, which serves as the enterprise data store in our demo setup and contains a collection of data. After these two data centers are connected using F5 XC Network Connect, a secure encrypted end-to-end connection is established between them. In our example, “test-ai-tbd” is in the data center in California where it hosts the RAG services, including embedding Large Language Model (LLM) and vector database, and it can now successfully connect to the enterprise data stores on NetApp ONTAP in the data center in Seattle: F5 Distributed Cloud App Connect F5 Distributed Cloud App Connect securely connects and delivers distributed applications and services across hybrid and multicloud environments. By utilizing F5 Distributed Cloud App Connect, we can direct the inference traffic through F5 Distributed Cloud's security layers to safeguard our inference endpoints. Red Hat OpenShift on Amazon Web Services (ROSA) is a fully managed service that allows users to develop, run, and scale applications in a native AWS environment. We can host our inference service on ROSA so that we can leverage the scalability, cost-effectiveness, and numerous benefits of AWS’s managed infrastructure services. For instance, we can host our inference service on ROSA by deploying Ollama with multiple AI/ML models: Or, we can enable Model Serving on Red Hat OpenShift AI (RHOAI). Red Hat OpenShift AI (RHOAI) is a flexible and scalable AI/ML platform builds on the capabilities of Red Hat OpenShift that facilitates collaboration among data scientists, engineers, and app developers. This platform allows them to serve, build, train, deploy, test, and monitor AI/ML models and applications either on-premise or in the cloud, fostering efficient innovation within organizations. In our example, we use Red Hat OpenShift AI (RHOAI) Model Serving on ROSA for our inference service: Once inference service is deployed on ROSA, we can utilize F5 Distributed Cloud to secure our inference endpoint by steering the inference traffic through F5 Distributed Cloud's security layers, which offers an extensive suite of features designed specifically for the security of modern AI/ML inference endpoints. This setup would allow us to scrutinize requests, implement policies for detected threats, and protect sensitive datasets before they reach the inferencing service hosted within ROSA. In our example, we setup a F5 Distributed Cloud HTTP Load Balancer (rhoai-llm-serving.f5-demo.com), and we advertise it to the CE in the datacenter in California only: We now reach our Red Hat OpenShift AI (RHOAI) inference endpoint through F5 Distributed Cloud: F5 Distributed Cloud Web App & API Protection F5 Distributed Cloud Web App & API Protection provides comprehensive sets of security features, and uniform observability and policy enforcement to protect apps and APIs across hybrid and multicloud environments. We utilize F5 Distributed Cloud App Connect to steer the inference traffic through F5 Distributed Cloud to secure our inference endpoint. In our example, we protect our Red Hat OpenShift AI (RHOAI) inference endpoint by rate-limiting the access, so that we can ensure no single client would exhaust the inference service: A "Too Many Requests" is received in the response when a single client repeatedly requests access to the inference service at a rate higher than the configured threshold: This is just one of the many security features to protect our inference service. Click here to find out more on Securing Model Serving in Red Hat OpenShift AI (on ROSA) with F5 Distributed Cloud API Security. Demonstration In a real-world scenario, the front-end application could be hosted on the cloud, or hosted at the edge, or served through F5 Distributed Cloud, offering flexible alternatives for efficient application delivery based on user preferences and specific needs. To illustrate how all the discussed components work seamlessly together, we simplify our example by deploying Open WebUI as the front-end application on the Red Hat OpenShift cluster in the data center in California, which includes RAG services. While a DPU or GPU could be used for improved performance, our setup utilizes a CPU for inferencing tasks. We connect our app to our enterprise data stores deployed on NetApp ONTAP in the data center in Seattle using F5 Distributed Cloud Network Connect, where we have a copy of "Chapter 1. About the Migration Toolkit for Virtualization" from Red Hat. These documents are processed and saved to the Vector DB: Our embedding Large Language Model (LLM) is Sentence-Transformers/all-MiniLM-L6-v2, and here is our RAG template: Instead of connecting to the inference endpoint on Red Hat OpenShift AI (RHOAI) on ROSA directly, we connect to the F5 Distributed Cloud HTTP Load Balancer (rhoai-llm-serving.f5-demo.com) from F5 Distributed Cloud App Connect: Previously, we asked, "What is MTV?“ and we never received a response related to Red Hat Migration Toolkit for Virtualization: Now, let's try asking the same question again with RAG services enabled: We finally received the response we had anticipated. Next, we use F5 Distributed Cloud Web App & API Protection to safeguard our Red Hat OpenShift AI (RHOAI) inference endpoint on ROSA by rate-limiting the access, thus preventing a single client from exhausting the inference service: As expected, we received "Too Many Requests" in the response on our app upon requesting the inference service at a rate greater than the set threshold: With F5 Distributed Cloud's real-time observability and security analytics from the F5 Distributed Console, we can proactively monitor for potential threats. For example, if necessary, we can block a client from accessing the inference service by adding it to the Blocked Clients List: As expected, this specific client is now unable to access the inference service: Summary Deploying and securing RAG for enterprise RAG-enabled AI applications in a multi-vendor, hybrid, and multi-cloud environment can present complex challenges. In collaboration with Red Hat OpenShift AI (RHOAI) and NetApp ONTAP, F5 Distributed Cloud provides an effortless solution that secures RAG components seamlessly for enterprise RAG-enabled AI applications.Demo Guide & Video Series for F5 Distributed Cloud Network Connect (Multi-Cloud Networking)
Exploring the core networking use-cases for F5 Distributed Cloud, the following demo guide and 3-part video series shows how to connect sample networks at Layer 3 (L3) and a sample application using Layer 7 (L7). If you've ever come across one of our Distributed Cloud, multi-cloud solutions and thought about trying it out for yourself but lack some of the precursor tools or awareness to do it, then this demo guide and video series is perfect for you. This shows every step to deploy a modern app in multiple cloud locations and make it available using the Distributed Cloud L7 HTTP LB, and also the L3 transit global network. Before going through the steps yourself, watch this 3-part video series illustrating the process in each location. Video: Module 1 - Distributed Cloud L7 Networking to AWS Video: Module 2 - Distributed Cloud L7 Networking to Azure Video: Module 3 - Distributed Cloud L3 between AWS and Azure via Global Network Ready to get your hands on? The following GitHub repository contains all the scripts and instructions performed in the videos above. https://github.com/f5devcentral/xcmcndemoguide Conclusion Deploying modern applications with services running in multiple locations and on different cloud providers is straight forward with Distributed Cloud Platform services. HTTP LB makes connects apps direct at the L7 Application Layer, and L3 transit routing via the Global Network makes it possible for apps to connect directly between multiple cloud locations. Resources Product Page: F5 Distributed Cloud - Multi-Cloud Networking GitHub repository: https://github.com/f5devcentral/xcmcndemoguide Interactive Product Experiences App Connect (Hybrid and Multicloud Layer7 load balancing) Network Connect (Hybrid and Multicloud Layer3 routing) Get Started - https://www.f5.com/cloud/pricingUse F5 Distributed Cloud to Connect Apps Running in Multiple Clusters and Sites
Introduction Modern apps are comprised of many smaller components and can take advantage of today’s agile computing landscape. One of the challenges IT Admins and Security Operations face is securely controlling access to all the compofnents of distributed apps while business development grows or changes hands with mergers and acquisitions, or as contracts change. F5 Distributed Cloud (F5 XC) makes it very easy to provide uniform access to distributed apps regardless of where the components live. Solution Overview Arcadia Finance is a distributed app with modules that run in multiple Kubernetes clusters and in multiple locations. To expedite development in a key part of the Arcadia Finance distributed app, the business has decided to outsource work on the Refer A Friend module. IT Ops must now relocate the Refer A Friend module to a separate location exclusive to the new contractor where its team of developers have access to work on it. Because the app is modular, IT has shared a copy of the Refer A Friend container to the developer, and now that it is up and running in the new site, traffic to the module needs to transition away from the one that had been developed in house to the one now managed by the contractor. Logical Topology Distributed App Overview The Refer A Friend endpoint is called by the Arcadia Finance frontend pod in a Kubernetes (K8s) cluster when a user of the service wants to invite a friend to join. The pod does this by making an HTTP request to the location “refer-a-friend.demo.internal/app3/”. The endpoint “refer-a-friend.demo.internal” is registered as a discoverable service in the K8s cluster using an F5 XC App Connect HTTP Load Balancer with its VIP configured to be advertised internally to specific sites, including the K8s cluster. F5 XC uses the cluster’s K8s management API to register service names and make them available anywhere within a global network belonging to a customer's tenant. Three sites are used by the company that owns Arcadia Finance to deliver the distributed app. The core of the app lives in a K8s cluster in Azure, the administration and monitoring of the app is in the customer’s legacy site in AWS. To maintain security, the new contractor only has access to GCP where they’ll continue developing the Refer A Friend module. An F5 XC global virtual network connects all three sites, and all three sites are in a site mesh group to streamline communication between the different app modules. Steps to deploy To reach the app externally, an App Connect HTTP Load Balancer policy is configured using an origin pool that connects to the K8s “frontend” service, and the origin pool uses a Kubernetes Site in F5 XC to access the frontend service. A second HTTP Load Balancer policy is configured with its origin pool, a static IP that lives in Azure and is accessed via a registered Azure VNET Site. When the Refer A Friend module is needed, a pod in the K8s cluster connects to the Refer A Friend internal VIP advertised by the HTTP Load Balancer policy. This connection is then tunneled by F5 XC to an endpoint where the module runs. With development to the Refer A Friend module turned over to the contractor, we only need to change the HTTP Load Balancer policy to use an origin pool located in the contractor’s Cloud GCP VPC Site. The origin policy for the GCP located module is nearly identical to the one used in Azure. Now when a user in the Arcadia App goes to refer a friend, the callout the app makes is now routed to the new location where it is managed and run by the new contractor. Demo Watch the following video for information about this solution and a walkthrough using the steps above in the F5 Distributed Cloud Console. Conclusion Using Distributed Cloud with modern day distributed apps, it’s almost too easy to route requests intended for a specific module to a new location regardless of the provider and provider specific requirements or the IP space the new module runs in. This is the true power of using Distributed Cloud to glue together modern day distributed apps.The App Delivery Fabric with Secure Multicloud Networking
This tutorial with accompanying workflow guide deploys customer edge sites and uses Distributed Cloud Multicloud Networking App Connect to establish a Secure MCN App Delivery Fabric, enabling only Layer7 app connectivity between two cloud sites. Manual and automation workflows show how to make this NetOps and DevOps task come to life.Kubernetes architecture options with F5 Distributed Cloud Services
Summary F5 Distributed Cloud Services (F5 XC) can both integrate with your existing Kubernetes (K8s) clusters and/or host a K8s workload itself. Within these distinctions, we have multiple architecture options. This article explores four major architectures in ascending order of sophistication and advantages. Architecture #1: External Load Balancer (Secure K8s Gateway) Architecture #2: CE as a pod (K8s site) Architecture #3: Managed Namespace (vK8s) Architecture #4: Managed K8s (mK8s) Kubernetes Architecture Options As K8s continues to grow, options for how we run K8s and integrate with existing K8s platforms continue to grow. F5 XC can both integrate with your existing K8s clusters and/or run a managed K8s platform itself. Multiple architectures exist within these offerings too, so I was thoroughly confused when I first heard about these possibilities. A colleague recently laid it out for me in a conversation: "Michael, listen up: XC can either integrate with your K8s platform, run inside your K8s platform, host virtual K8s (Namespace-aaS), or run a K8s platform in your environment." I replied, "That's great. Now I have a mental model for differentiating between architecture options." This article will overview these architectures and provide 101-level context: when, how, and why would you implement these options? Side note 1: F5 XC concepts and terms F5 XC is a global platform that can provide networking and app delivery services, as well as compute (K8s workloads). We call each of our global PoP's a Regional Edge (RE). RE's are highly meshed to form the backbone of the global platform. They connect your sites, they can expose your services to the Internet, and they can run workloads. This platform is extensible into your data center by running one or more XC Nodes in your network, also called a Customer Edge (CE). A CE is a compute node in your network that registers to our global control plane and is then managed by a customer as SaaS. The registration of one or more CE's creates a customer site in F5 XC. A CE can run on a hypervisor (VMWare/KVM/Etc), a Hyperscaler (AWS, Azure, GCP, etc), baremetal, or even as a k8s pod, and can be deployed in HA clusters. XC Mesh functionality provides connectivity between sites, security services, and observability. Optionally, in addition, XC App Stack functionality allows a large and arbitrary number of managed clusters to be logically grouped into a virtual site with a single K8s mgmt interface. So where Mesh services provide the networking, App Stack services provide the Kubernetes compute mgmt. Our first 2 architectures require Mesh services only, and our last two require App Stack. Side note 2: Service-to-service communication I'm often asked how to allow services between clusters to communicate with each other. This is possible and easy with XC. Each site can publish services to every other site, including K8s sites. This means that any K8s service can be reachable from other sites you choose. And this can be true in any of the architectures below, although more granular controls are possible with the more sophisticated architectures. I'll explore this common question more in a separate article. Architecture 1: External Load Balancer (Secure K8s Gateway) In a Secure Kubernetes Gateway architecture, you have integration with your existing K8s platform, using the XC node as the external load balancer for your K8s cluster. In this scenario, you create a ServiceAccount and kubeconfig file to configure XC. The XC node then performs service discovery against your K8s API server. I've covered this process in a previous article, but the advantage is that you can integrate with existing K8s platforms. This allows exposing both NodePort and ClusterIP services via the XC node. XC is not hosting any workloads in this architecture, but it is exposing your services to your local network, or remote sites, or the Internet. In the diagram above, I show a web application being accesssed from a remote site (and/or the Internet) where the origin pool is a NodePort service discovered in a K8s cluster. Architecture 2: Run a site within a K8s cluster (K8s site type) Creating a K8s site is easy - just deploy a single manifest found here. This file deploys multiple resources in your cluster, and together these resources work to provide the services of a CE, and create a customer site. I've heard this referred to as "running a CE inside of K8s" or "running your CE as a pod". However, when I say "CE node" I'm usually referring to a discreet compute node like a VM or piece of hardware; this architecture is actually a group of pods and related resources that run within K8s to create a XC customer site. With XC running inside your existing cluster, you can expose services within the cluster by DNS name because the site will resolve these from within the cluster. Your service can then be exposed anywhere by the F5 XC platform. This is similar to Architecture 1 above, but with this model, your site is simply a group of pods within K8s. An advantage here is the ability to expose services of other types (e.g. ClusterIP). A site deployed into a K8s cluster will only support Mesh functionality and does not support AppStack functionality (i.e., you cannot run a cluster within your cluster). In this architecture, XC acts as a K8s ingress controller with built-in application security. It also enables Mesh features, such as publishing of other sites' services on this site, and publishing of this site's discovered services on other sites. Architecture 3: vK8s (Namespace-as-a-Service) If the services you use include AppStack capabilities, then architectures #3 and #4 are possible for you. In these scenarios, our XC node actually runs your K8s on your workloads. We are no longer integrating XC with your existing K8s platform. XC is the platform. A simple way to run K8s workloads is to use a virtual k8s (vK8s) architecture. This could be referred to as a "managed Namespace" because by creating a vK8s object in XC you get a single namespace in a virtual cluster. Your Namespace can be fully hosted (deployed to RE's) or run on your VM's (CE's), or both. Your kubeconfig file will allow access to your Namespace via the hosted API server. Via your regular kubectl CLI (or via the web console) you can create/delete/manage K8s resources (Deployments, Services, Secrets, ServiceAccounts, etc) and view application resource metrics. This is great if you have workloads that you want to deploy to remote regions where you do not have infrastructure and would prefer to run in F5's RE's, or if you have disparate clusters across multiple sites and you'd like to manage multiple K8s clusters via a single centralized, virtual cluster. Best practice guard rails for vK8s With a vK8s architecture, you don't have your own cluster, but rather a managed Namespace. So there are some restrictions (for example, you cannot run a container as root, bind to a privileged port, or to the Host network). You cannot create CRD's, ClusterRoles, PodSecurityPolicies, or Namespaces, so K8s operators are not supported. In short, you don't have a managed cluster, but a managed Namespace on a virtual cluster. Architecture 4: mK8s (Managed K8s) In managed k8s (mk8s, also known as physical K8s or pk8s) deployment, we have an enterprise-level K8s distribution that is run at your site. This means you can use XC to deploy/manage/upgrade K8s infrastructure, but you manage the Kubernetes resources. The benefits include what is typical for 3rd-party K8s mgmt solutions, but also some key differentiators: multi-cloud, with automation for Azure, AWS, and GCP environments consumed by you as SaaS enterprise-level traffic control natively allows a large and arbitrary number of managed clusters to be logically managed with a single K8s mgmt interface You can enable kubectl access against your local cluster and disable the hosted API server, so your kubeconfig file can point to a global URL or a local endpoint on-prem. Another benefit of mK8s is that you are running a full K8s cluster at your site, not just a Namespace in a virtual cluster. The restrictions that apply to vK8s (see above) do not apply to mK8s, so you could run privileged pods if required, use Operators that make use of ClusterRoles and CRDs, and perform other tasks that require cluster-wide access. Traffic management controls with mK8s Because your workloads run in a cluster managed by XC, we can apply more sophisticated and native policies to K8s traffic than non-managed clusters in earlier architectures: Service isolation can be enforced within the cluster, so that pods in a given namespace cannot communicate with services outside of that namespace, by default. More service-to-service controls exist so that you can decide which services can reach with other services with more granularity. Egress control can be natively enforced for outbound traffic from the cluster, by namespace, labels, IP ranges, or other methods. E.g.: Svc A can reach myapi.example.com but no other Internet service. WAF policies, bot defense, L3/4 policies, etc—all of these policies that you have typically applied with network firewalls, WAF's, etc—can be applied natively within the platform. This architecture took me a long time to understand, and longer to fully appreciate. But once you have run your workloads natively on a managed K8s platform that is connected to a global backbone and capable of performing network and application delivery within the platform, the security and traffic mgmt benefits become very compelling. Conclusion: As K8s continues to expand, management solutions of your clusters make it possible to secure your K8s services, whether they are managed by XC or exist in disparate clusters. With F5 XC as a global platform consumed as a service—not a discreet installation managed by you—the available architectures here are unique and therefore can accommodate the diverse (and changing!) ways we see K8s run today. Related Articles Securely connecting Kubernetes Microservices with F5 Distributed Cloud Multi-cluster Multi-cloud Networking for K8s with F5 Distributed Cloud - Architecture Pattern Multiple Kubernetes Clusters and Path-Based Routing with F5 Distributed CloudDeploying F5 Distributed Cloud (XC) Services in Cisco ACI - Layer Three Attached Deployment
Introduction F5 Distributed Cloud (XC) Services are SaaS-based security, networking, and application management services that can be deployed across multi-cloud, on-premises, and edge locations. This article will show you how you can deploy F5 Distributed Cloud Customer Edge (CE) site in Cisco Application Centric Infrastructure (ACI) so that you can securely connect your application in Hybrid Multi-Cloud environment. XC Layer Three Attached CE in Cisco ACI A F5 Distributed Cloud Customer Edge (CE) site can be deployed with Layer Three Attached in Cisco ACI environment using Cisco ACI L3Out. As a reminder, Layer Three Attached is one of the deployment models to get traffic to/from a F5 Distributed Cloud CE site, where the CE can be a single node or a three nodes cluster. Static routing and BGP are both supported in the Layer Three Attached deployment model. When a Layer Three Attached CE site is deployed in Cisco ACI environment using Cisco ACI L3Out, routes can be exchanged between them via static routing or BGP. In this article, we will focus on BGP peering between Layer Three Attached CE site and Cisco ACI Fabric. XC BGP Configuration BGP configuration on XC is simple and it only takes a couple steps to complete: 1) Go to "Multi-Cloud Network Connect" -> "Networking" -> "BGPs". *Note: XC homepage is role based, and to be able to configure BGP, "Advanced User" is required. 2) "Add BGP" to fill out the site specific info, such as which CE Site to run BGP, its BGP AS number etc., and "Add Peers" to include its BGP peers’ info. *Note: XC supports direct connection for BGP peering IP reachability only. XC Layer Three Attached CE in ACI Example In this section, we will use an example to show you how to successfully bring up BGP peering between a F5 XC Layer Three Attached CE site and a Cisco ACI Fabric so that you can securely connect your application in Hybrid Multi-Cloud environment. Topology In our example, CE is a three nodes cluster (Master-0, Master-1 and Master-2) that has a VIP 10.10.122.122/32 with workloads, 10.131.111.66 and 10.131.111.77, in the cloud (AWS): The CE connects to the ACI Fabric via a virtual port channel (vPC) that spans across two ACI boarder leaf switches. CE and ACI Fabric are eBGP peers via an ACI L3Out SVI for routes exchange. CE is eBGP peered to both ACI boarder leaf switches, so that in case one of them is down (expectedly or unexpectedly), CE can still continue to exchange routes with the ACI boarder leaf switch that remains up and VIP reachability will not be affected. XC BGP Configuration First, let us look at the XC BGP configuration ("Multi-Cloud Network Connect" -> "Networking" -> "BGPs"): We "Add BGP" of "jy-site2-cluster" with site specific BGP info along with a total of six eBGP peers (each CE node has two eBGP peers; one to each ACI boarder leaf switch): We "Add Item" to specify each of the six eBPG peers’ info: Example reference - ACI BGP configuration: XC BGP Peering Status There are a couple of ways to check the BGP peering status on the F5 Distributed Cloud Console: Option 1 Go to "Multi-Cloud Network Connect" -> "Networking" -> "BGPs" -> "Show Status" from the selected CE site to bring up the "Status Objects" page. The "Status Objects" page provides a summary of the BGP status from each of the CE nodes. In our example, all three CE nodes from "jy-site2-cluster" are cleared with "0 Failed Conditions" (Green): We can simply click on a CE node UID to further look into the BGP status from the selected CE node with all of its BGP peers. Here, we clicked on the UID of CE node Master-2 (172.18.128.14) and we can see it has two eBGP peers: 172.18.128.11 (ACI boarder leaf switch 1) and 172.18.128.12 (ACI boarder leaf switch 2), and both of them are Up: Here is the BGP status from the other two CE nodes - Master-0 (172.18.128.6) and Master-1 (172.18.128.10): For reference, here is an example of a CE node with "Failed Conditions" (Red) due to one of its BGP peers is down: Option 2 Go to "Multi-Cloud Network Connect" -> "Overview" -> "Sites" -> "Tools" -> "Show BGP peers" to bring up the BGP peers status info from all CE nodes from the selected site. Here, we can see the same BGP status of CE node master-2 (172.18.128.14) which has two eBGP peers: 172.18.128.11 (ACI boarder leaf switch 1) and 172.18.128.12 (ACI boarder leaf switch 2), and both of them are Up: Here is the output of the other two CE nodes - Master-0 (172.18.128.6) and Master-1 (172.18.128.10): Example reference - ACI BGP peering status: XC BGP Routes Status To check the BGP routes, both received and advertised routes, go to "Multi-Cloud Network Connect" -> "Overview" -> "Sites" -> "Tools" -> "Show BGP routes" from the selected CE sites: In our example, we see all three CE nodes (Master-0, Master-1 and Master-2) advertised (exported) 10.10.122.122/32 to both of its BPG peers: 172.18.128.11 (ACI boarder leaf switch 1) and 172.18.128.12 (ACI boarder leaf switch 2), while received (imported) 172.18.188.0/24 from them: Now, if we check the ACI Fabric, we should see both 172.18.128.11 (ACI boarder leaf switch 1) and 172.18.128.12 (ACI boarder leaf switch 2) advertised 172.18.188.0/24 to all three CE nodes, while received 10.10.122.122/32 from all three of them (note "|" for multipath in the output): XC Routes Status To view the routing table of a CE node (or all CE nodes at once), we can simply select "Show routes": Based on the BGP routing table in our example (shown earlier), we should see each CE node has two Equal Cost Multi-Path (ECMP) installed in the routing table for 172.18.188.0/24: one to 172.18.128.11 (ACI boarder leaf switch 1) and one to 172.18.128.12 (ACI boarder leaf switch 2) as the next-hop, and we do (note "ECMP" for multipath in the output): Now, if we check the ACI Fabric, each of the ACI boarder leaf switch should have three ECMP installed in the routing table for 10.10.122.122: one to each CE node (172.18.128.6, 172.18.128.10 and 172.18.128.14) as the next-hop, and we do: Validation We can now securely connect our application in Hybrid Multi-Cloud environment: *Note: After F5 XC is deployed, we also use F5 XC DNS as our primary nameserver: To check the requests on the F5 Distributed Cloud Console, go to "Multi-Cloud Network Connect" -> "Sites" -> "Requests" from the selected CE site: Summary A F5 Distributed Cloud Customer Edge (CE) site can be deployed with Layer Three Attached deployment model in Cisco ACI environment. Both static routing and BGP are supported in the Layer Three Attached deployment model and can be easily configured on F5 Distributed Cloud Console with just a few clicks. With F5 Distributed Cloud Customer Edge (CE) site deployment, you can securely connect your application in Hybrid Multi-Cloud environment quickly and efficiently. Next Check out this video for some examples of Layer Three Attached CE use cases in Cisco ACI: Related Resources *On-Demand Webinar* Deploying F5 Distributed Cloud Services in Cisco ACI F5 Distributed Cloud (XC) Global Applications Load Balancing in Cisco ACI Deploying F5 Distributed Cloud (XC) Services in Cisco ACI - Layer Two Attached Deployment Customer Edge Site - Deployment & Routing Options Cisco ACI L3Out White Paper
