Announcing F5 NGINX Gateway Fabric 1.4.0 with IPv6 and TLS Passthrough
We announced the next release of F5 NGINX Gateway Fabric version 1.4.0 which includes a lot of smaller but very necessary features. This allows us to dedicate more time to advancing our non-functional testing framework and ensuring we maintain top performance across releases. Nevertheless, we have some great highlights of this release: IPv6 support TLS passthrough (via TLSRoute) Server zone metrics Ability to add custom pod annotations Plenty of bug fixes! During this release cycle, we discovered a bug around our custom policies that occurred when you had the same path for more than one Route: The policy would not be applied to either Route. For this release, we’ve decided to enforce a restriction so that policies cannot be applied when two or more routes share the same path. However, we are pursuing a long-term solution to lift this restriction on this edge case, as we understand that use cases that route based on header, query parameter, or other request attributes on the same path do exist. IPv6 Support While most Kubernetes clusters are still utilizing IPv4, we recognized that anyone employing a IPv6 cluster would have no ability to deploy NGINX Gateway Fabric. Thus, we implemented a simple feature to dual IPv4/IPv6 networking for NGINX Gateway Fabric. This option is enabled by default, so you can simply install as normal on an IPv6 cluster. TLS Passthrough New with 1.4 is TLSRoute support. This Route type enables the TLS Passthrough use case and is similar to setting up an HTTPRoute. This allows you to pass encrypted traffic through NGINX Gateway Fabric where it is terminated by your backend application, ensuring end-to-end encryption. As most information passes through NGINX Gateway Fabric with this route, setup is easy. You can enable TLS passthrough for any application using our guide available here. Non-Functional Testing This release marks the completion of automating our non-functional testing that we execute before each release. If you are unfamiliar with these tests, our team runs NGINX Gateway Fabric through a series of scenarios, non-functional tests, to test if our performance is regressing or improving from previous releases. As an infrastructure product that you rely on, it is our top priority to ensure that stability and performance are not compromised as new features are released. The results of all non-functional testing are available in the GitHub repository for anyone to see and should give you an idea of how well NGINX Gateway Fabric performs in general and across releases. What’s Next NGINX Gateway Fabric 1.5.0 will bring NGINX code snippets to the Gateway API with a first-class Upstream Settings policy to configure keepalive connections and NGINX zone size. If you are familiar with NGINX or find that you need to use a feature that NGINX provides that is not yet available via a Gateway API extension, you can put a NGINX code snippet within a SnippetFilter to apply NGINX configuration to a Route rule. You will even be able to use the feature to load other modules NGINX provides and leverage the vast wealth of NGINX functionality. We will still be providing many NGINX features via first-class policies and filters, such as the Upstream Settings policy, as they allow us to handle much of the complexity of translating to Gateway API for you. These custom policies and filters allow us to handle a lot of the complexity of applying NGINX config across the Gateway API framework for you. The Upstream Settings policy can set upstream management directives that are unable to be applied via snippets effectively. We will continue to deliver these custom policies and filters across all of our releases, in addition to new Gateway API resources and NGINX Gateway Fabric specific features. You can see a preview of the full snippet design here, though not all features may be implemented in one release cycle. For more information on our strategy towards first-class NGINX customization via Gateway API extensions, see our full enhancement proposalhere. Resources For the complete changelog for NGINX Gateway Fabric 1.4.0, see the Release Notes. To try NGINX Gateway Fabric for Kubernetes with NGINX Plus, start your free 30-day trial today or contact us to discuss your use cases. If you would like to get involved, see what is coming next, or see the source code for NGINX Gateway Fabric, check out our repository on GitHub! We have weekly community meetings on Tuesdays at 9:30AM Pacific/12:30PM Eastern/5:30PM GMT. The meeting link, updates, agenda, and notes are on the NGINX Gateway Fabric Meeting Calendar. Links are also always available from our GitHub readme.Announcing F5 NGINX Gateway Fabric 1.3.0 with Tracing, GRPCRoute, and Client Settings
The release of NGINX Gateway Fabric version 1.3.0, introduces plenty of highly requested features and improvements. GRPCRoutes are now supported to manage gRPC traffic, similar to the handling of HTTPRoute. The update includes new custom policies like ClientSettingsPolicy for client request configurations and ObservabilityPolicy for enabling application tracing with OpenTelemetry support. The GRPCRoute allows for efficient routing, header modifications, traffic weighting, and error conversion from HTTP to gRPC. We will explain how to set up NGINX Gateway Fabric to manage gRPC traffic using a Gateway and a GRPCRoute, providing a detailed example of the setup. It also outlines how to enable tracing through the NginxProxy resource and ObservabilityPolicy, emphasizing a selective approach to tracing to avoid data overload. Additionally, the ClientSettingsPolicy allows for the customization of NGINX directives at the Gateway or Route level, giving users control over certain NGINX behaviors with the possibility of overriding Gateway defaults at the Route level. Looking ahead, the NGINX Gateway Fabric team plans to work on TLS Passthrough, IPv6, and improvements to the testing suite, while preparing for larger updates like NGINX directive customization and separation of data and control planes. Check the end of the article to see how to get involved in the development process through GitHub and participate in bi-weekly community meetings. Further resources and links are also provided within.
XML Threat Prevention
Where should security live? The divide between operations and application developers is pretty wide, especially when it comes to defining who should be ultimately responsible for application security. Mike Fratto and I have often had lively discussions (read: arguments) about whether security is the responsibility of the developer or the network and security administrators. It's wholly inappropriate to recreate any of these discussions here, as they often devolve to including the words your mother said not to use in public. 'Nuff said. The truth is that when XML enters the picture then the responsibility for securing that traffic has to be borne by both the network/security administrators and the developers. While there is certainly good reason to expect that developers are doing simply security checks for buffer overflows, length restrictions on incoming data, and strong typing, the fact is that there are some attacks in XML that make it completely impractical to check for in the code. Let's take a couple of attack types as examples. XML Entity Expansion This attack is a million laughs, or at least a million or more bytes of memory. Applications need to parse XML in order to manipulate it, so the first thing that happens when XML hits an application is that it is parsed - before the developer even has a chance to check it. In an application server this is generally done before the arguments to the specific operation being invoked are marshaled - meaning it is the application server, not the developer that is responsible for handling this type of attack. These messages can be used to force recursive entity expansion or other repeated processing that exhausts server resources. The most common example of this type of attack is the "billion laughs" attack, which is widely available. The CPU is monopolized while the entities are being expanded, and each entity takes up X amount of memory - eventually consuming all available resources and effectively preventing legitimate traffic from being processed. It's essentially a DoS (Denial of Service) attack. ... ]> &ha128; It is accepted that almost all traditional DoS attacks (ping of death, teardrop, etc...) should be handled by a perimeter security device - a firewall oran application delivery controller - so why should a DoS attack that is perpetrated through XML be any different? It shouldn't. This isn't a developer problem, it's a parser problem and for the most part developers have little or no control over the behavior of the parser used by the application server. The application admin, however, can configure most modern parsers to prevent this type of attack, but that's assuming that their parser is modern and can be configured to handle it. Of course then you have to wonder what happens if that arbitrary limit inhibits processing of valid traffic? Yeah, it's a serious problem. SQL Injection SQL Injection is one of the most commonly perpetrated attacks via web-based applications. It consists basically of inserting SQL code into string-based fields which the attacks thinks (or knows) will be passed to a database as part of an SQL query. This type of attack can easily be accomplished via XML as well simply by inserting the appropriate SQL code into a string element. Aha! The developer can stop this one, you're thinking. After all, the developer has the string and builds the SQL that will be executed, so he can just check for it before he builds the string and sends it off for execution. While this is certainly true, there are myriad combinations of SQL commands that might induce the database to return more data than it should, or to return sensitive data not authorized to the user. This extensive list of commands and combinations of commands would need to be searched for in each and every parameter used to create an SQL query and on every call to the database. That's a lot of extra code and a lot of extra processing - which is going to slow down the application and impede performance. And when a new attack is discovered, each and every function and application needs to be updated, tested, and re-deployed. I'm fairly certain developers have better ways to spend their time than updating parameter checking in every function in every application they have in production. And we won't even talk about third-party applications and the dangers inherent in that scenario. One of the goals of SOA is engendering reuse, and this is one of the best examples of taking advantage of reuse in order to ensure consistent behavior between applications and to reduce the lengthy development cycle required to update, test, and redeploy whenever a new attack is discovered. By placing the onus for keeping this kind of attack from reaching the server on an edge device such as an application firewall like F5's application firewall, updates to address new attacks are immediately applied to all applications and there is no need to recode and redeploy applications. Although there are some aspects of security that are certainly best left to the developer, there are other aspects of security that are better deployed in the network. It's the most effective plan in terms of effort, cost, and consistent behavior where applications are concerned. Imbibing: Mountain Dew Technorati tags: security, application security, application firewall, XML, developers, networking, application deliveryDevops Proverb: Process Practice Makes Perfect
#devops Tools for automating – and optimizing – processes are a must-have for enabling continuous delivery of application deployments Some idioms are cross-cultural and cross-temporal. They transcend cultures and time, remaining relevant no matter where or when they are spoken. These idioms are often referred to as proverbs, which carries with it a sense of enduring wisdom. One such idiom, “practice makes perfect”, can be found in just about every culture in some form. In Chinese, for example, the idiom is apparently properly read as “familiarity through doing creates high proficiency”, i.e. practice makes perfect. This is a central tenet of devops, particularly where optimization of operational processes is concerned. The more often you execute a process, the more likely you are to get better at it and discover what activities (steps) within that process may need tweaking or changes or improvements. Ergo, optimization. This tenet grows out of the agile methodology adopted by devops: application release cycles should be nearly continuous, with both developers and operations iterating over the same process – develop, test, deploy – with a high level of frequency. Eventually (one hopes) we achieve process perfection – or at least what we might call process perfection: repeatable, consistent deployment success. It is implied that in order to achieve this many processes will be automated, once we have discovered and defined them in such a way as to enable them to be automated. But how does one automate a process such as an application release cycle? Business Process Management (BPM) works well for automating business workflows; such systems include adapters and plug-ins that allow communication between systems as well as people. But these systems are not designed for operations; there are no web servers or databases or Load balancer adapters for even the most widely adopted BPM systems. One such solution can be found in Electric Cloud with its recently announced ElectricDeploy. Process Automation for Operations ElectricDeploy is built upon a more well known product from Electric Cloud (well, more well-known in developer circles, at least) known as ElectricCommander, a build-test-deploy application deployment system. Its interface presents applications in terms of tiers – but extends beyond the traditional three-tiers associated with development to include infrastructure services such as – you guessed it – load balancers (yes, including BIG-IP) and virtual infrastructure. The view enables operators to create the tiers appropriate to applications and then orchestrate deployment processes through fairly predictable phases – test, QA, pre-production and production. What’s hawesome about the tools is the ability to control the process – to rollback, to restore, and even debug. The debugging capabilities enable operators to stop at specified tasks in order to examine output from systems, check log files, etc..to ensure the process is executing properly. While it’s not able to perform “step into” debugging (stepping into the configuration of the load balancer, for example, and manually executing line by line changes) it can perform what developers know as “step over” debugging, which means you can step through a process at the highest layer and pause at break points, but you can’t yet dive into the actual task. Still, the ability to pause an executing process and examine output, as well as rollback or restore specific process versions (yes, it versions the processes as well, just as you’d expect) would certainly be a boon to operations in the quest to adopt tools and methodologies from development that can aid them in improving time and consistency of deployments. The tool also enables operations to determine what is failure during a deployment. For example, you may want to stop and rollback the deployment when a server fails to launch if your deployment only comprises 2 or 3 servers, but when it comprises 1000s it may be acceptable that a few fail to launch. Success and failure of individual tasks as well as the overall process are defined by the organization and allow for flexibility. This is more than just automation, it’s managed automation; it’s agile in action; it’s focusing on the processes, not the plumbing. MANUAL still RULES Electric Cloud recently (June 2012) conducted a survey on the “state of application deployments today” and found some not unexpected but still frustrating results including that 75% of application deployments are still performed manually or with little to no automation. While automation may not be the goal of devops, but it is a tool enabling operations to achieve its goals and thus it should be more broadly considered as standard operating procedure to automate as much of the deployment process as possible. This is particularly true when operations fully adopts not only the premise of devops but the conclusion resulting from its agile roots. Tighter, faster, more frequent release cycles necessarily puts an additional burden on operations to execute the same processes over and over again. Trying to manually accomplish this may be setting operations up for failure and leave operations focused more on simply going through the motions and getting the application into production successfully than on streamlining and optimizing the processes they are executing. Electric Cloud’s ElectricDeploy is one of the ways in which process optimization can be achieved, and justifies its purchase by operations by promising to enable better control over application deployment processes across development and infrastructure. Devops is a Verb 1024 Words: The Devops Butterfly Effect Devops is Not All About Automation Application Security is a Stack Capacity in the Cloud: Concurrency versus Connections Ecosystems are Always in Flux The Pythagorean Theorem of Operational RiskInside Look - PCoIP Proxy for VMware Horizon View
I sit down with F5 Solution Architect Paul Pindell to get an inside look at BIG-IP's native support for VMware's PCoIP protocol. He reviews the architecture, business value and gives a great demo on how to configure BIG-IP. BIG-IP APM offers full proxy support for PC-over-IP (PCoIP), a leading virtual desktop infrastructure (VDI) protocol. F5 is the first to provide this functionality which allows organizations to simplify their VMware Horizon View architectures. Combining PCoIP proxy with the power of the BIG-IP platform delivers hardened security and increased scalability for end-user computing. In addition to PCoIP, F5 supports a number of other VDI solutions, giving customers flexibility in designing and deploying their network infrastructure. ps Related: F5 Friday: Simple, Scalable and Secure PCoIP for VMware Horizon View Solutions for VMware applications F5's YouTube Channel In 5 Minutes or Less Series (24 videos – over 2 hours of In 5 Fun) Inside Look Series Life@F5 Series Technorati Tags: vdi,PCoIP,VMware,Access,Applications,Infrastructure,Performance,Security,Virtualization,silva,video,inside look,big-ip,apm Connect with Peter: Connect with F5:The Concise Guide to Proxies
We often mention that the benefits derived from some application delivery controllers are due to the nature of being a full proxy. And in the same breath we might mention reverse, half, and forward proxies, which makes the technology sound more like a description of the positions on a sports team than an application delivery solution. So what does these terms really mean? Here's the lowdown on the different kinds of proxies in one concise guide. PROXIES Proxies (often called intermediaries in the SOA world) are hardware or software solutions that sit between the client and the server and do something to requests and sometimes responses. The most often heard use of the term proxy is in conjunction with anonymizing Web surfing. That's because proxies sit between your browser and your desired destination and proxy the connection; that is you talk to the proxy while the proxy talks to the web server and neither you nor the web server know about each other. Proxies are not all the same. Some are half proxies, some are full proxies; some are forward and some are reverse. Yes, that came excruciatingly close to sounding like a Dr. Seuss book. (Go ahead, you know you want to. You may even remember this from .. .well, when it was first circulated.) FORWARD PROXIES Forward proxies are probably the most well known of all proxies, primarily because most folks have dealt with them either directly or indirectly. Forward proxies are those proxies that sit between two networks, usually a private internal network and the public Internet. Forward proxies have also traditionally been employed by large service providers as a bridge between their isolated network of subscribers and the public Internet, such as CompuServe and AOL in days gone by. These are often referred to as "mega-proxies" because they managed such high volumes of traffic. Forward proxies are generally HTTP (Web) proxies that provide a number of services but primarily focus on web content filtering and caching services. These forward proxies often include authentication and authorization as a part of their product to provide more control over access to public content. If you've ever gotten a web page that says "Your request has been denied by blah blah blah. If you think this is an error please contact the help desk/your administrator" then you've probably used a forward proxy. REVERSE PROXIES A reverse proxy is less well known, generally because we don't use the term anymore to describe products used as such. Load balancers (application delivery controllers) and caches are good examples of reverse proxies. Reverse proxies sit in front of web and application servers and process requests for applications and content coming in from the public Internet to the internal, private network. This is the primary reason for the appellation "reverse" proxy - to differentiate it from a proxy that handles outbound requests. Reverse proxies are also generally focused on HTTP but in recent years have expanded to include a number of other protocols commonly used on the web such as streaming audio (RTSP), file transfers (FTP), and generally any application protocol capable of being delivered via UDP or TCP. HALF PROXIES Half-proxy is a description of the way in which a proxy, reverse or forward, handles connections. There are two uses of the term half-proxy: one describing a deployment configuration that affects the way connections are handled and one that describes simply the difference between a first and subsequent connections. The deployment focused definition of half-proxy is associated with a direct server return (DSR) configuration. Requests are proxied by the device, but the responses do not return through the device, but rather are sent directly to the client. For some types of data - particularly streaming protocols - this configuration results in improved performance. This configuration is known as a half-proxy because only half the connection (incoming) is proxied while the other half, the response, is not. The second use of the term "half-proxy" describes a solution in which the proxy performs what is known as delayed binding in order to provide additional functionality. This allows the proxy to examine the request before determining where to send it. Once the proxy determines where to route the request, the connection between the client and the server are "stitched" together. This is referred to as a half-proxy because the initial TCP handshaking and first requests are proxied by the solution, but subsequently forwarded without interception. Half proxies can look at incoming requests in order to determine where the connection should be sent and can even use techniques to perform layer 7 inspection, but they are rarely capable of examining the responses. Almost all half-proxies fall into the category of reverse proxies. FULL PROXIES Full proxy is also a description of the way in which a proxy, reverse or forward, handles connections. A full proxy maintains two separate connections - one between itself and the client and one between itself and the destination server. A full proxy completely understands the protocols, and is itself an endpoint and an originator for the protocols. Full proxies are named because they completely proxy connections - incoming and outgoing. Because the full proxy is an actual protocol endpoint, it must fully implement the protocols as both a client and a server (a packet-based design does not). This also means the full proxy can have its own TCP connection behavior, such as buffering, retransmits, and TCP options. With a full proxy, each connection is unique; each can have its own TCP connection behavior. This means that a client connecting to the full proxy device would likely have different connection behavior than the full proxy might use for communicating with servers. Full proxies can look at incoming requests and outbound responses and can manipulate both if the solution allows it. Many reverse and forward proxies use a full proxy model today. There is no guarantee that a given solution is a full proxy, so you should always ask your solution provider if it is important to you that the solution is a full proxy.Programmability in the Network: Canary Deployments
#devops The canary deployment pattern is another means of enabling continuous delivery. Deployment patterns (or as I like to call them of late, devops patterns) are good examples of how devops can put into place systems and tools that enable continuous delivery to be, well, continuous. The goal of these patterns is, for the most part, to make sure operations can smoothly move features, functions, releases or applications into production. We've previously looked at the Blue Green deployment pattern and today we're going to look at a variation: Canary deployments. Canary deployments are applicable when you're running a cluster of servers. In other words, you've got lots and lots of (probably active right now while you're considering pushing that next release) users. What you don't want is to do the traditional "we're sorry, we're down for maintenance, here's a picture of a funny squirrel to amuse you while you wait" maintenance page. You want to be able to roll out the new release without disruption. Yeah, that's quite the ask, isn't it? The Canary deployment pattern is an incremental upgrade methodology. First, the build is pushed to a small set of servers to which only a select group of users are directed. If that goes well, the release is pushed to a larger set of servers with a limited set of users. Finally, if that goes well, then the release is pushed out to all servers and all users. If issues occur at any stage, the release is halted - it goes no further. Hence the naming of the pattern - after the miner's canary, used because "its demise provided a warning of dangerous levels of toxic gases". The trick to implementing this pattern is two fold: first, being able to group the servers used in each step into discrete pools and second, the ability to direct specific sets of users to the appropriate pools. Both capabilities requires the ability to execute some logic to perform user-based load balancing. Nolio, in its first Devops Best Practices video, implements Canary deployments by manipulating the pools of servers at the load balancing tier, removing them to upgrade and then reinserting them for testing before moving onto the next phase. If your load balancing solution is programmable, there's no need to actually remove them as you can simply insert logic to remove them from being selected until they've been upgraded. You can also then insert the logic to determine which users are directed to which pool of servers. If the load balancing platform is really programmable, you can even extend that to determination to querying a database to determine user inclusion in certain groups, such as those you might use to perform AB testing. Such logic might base the decision on IP address (not the best option but an option) or later, when you're actually rolling out to a percentage of users you can write logic that randomly selects users based on location or their user name - like sharding, only in reverse - or pretty much anything you can think of. You can even split that further if you're rolling out an update to an API that's used by both mobile and traditional clients, to catch both or neither or specific types in an orderly fashion so you can test methodically - because you want to test methodically when you're using live users as test subjects. The beauty of this pattern is that allows continuous delivery. Users are never disrupted (if you do it right) and the upgrade occurs in a safely staged, incremental fashion. That enables you to back out quickly if necessary, because you do have a back button plan, right? Right?DNSSEC: Is Your Infrastructure Ready?
A few months ago, we teamed with Infoblox for a DNSSEC webinar. Jonathan George, F5 Product Marketing Manager, leads with myself and Cricket Liu of Infoblox as background noise. He’s a blast as always and certainly knows his DNS. So, learn how F5 enables you to deploy DNSSEC quickly and easily into an existing GSLB environment with BIG-IP Global Traffic Manager (GTM). BIG-IP GTM streamlines encryption key generation and distribution by dynamically signing DNS responses in real-time. Running time: 49:20 </p> <p>ps</p> <p>Resources:</p> <ul> <li><a href="http://www.f5.com/news-press-events/web-media/" _fcksavedurl="http://www.f5.com/news-press-events/web-media/">F5 Web Media</a></li> <li><a href="http://www.youtube.com/user/f5networksinc" _fcksavedurl="http://www.youtube.com/user/f5networksinc">F5 YouTube Channel</a></li> <li><a href="http://www.f5.com/products/big-ip/global-traffic-manager.html" _fcksavedurl="http://www.f5.com/products/big-ip/global-traffic-manager.html">BIG-IP GTM</a></li> <li><a href="http://www.f5.com/pdf/white-papers/dnssec-wp.pdf" _fcksavedurl="http://www.f5.com/pdf/white-papers/dnssec-wp.pdf">DNSSEC: The Antidote to DNS Cache Poisoning and Other DNS Attacks (whitepaper)</a> | <a href="http://devcentral.f5.com/s/weblogs/interviews/archive/2009/12/04/audio-tech-brief-dnssec-the-antidote-to-dns.aspx" _fcksavedurl="http://devcentral.f5.com/s/weblogs/interviews/archive/2009/12/04/audio-tech-brief-dnssec-the-antidote-to-dns.aspx">Audio</a></li> <li><a href="http://www.cricketondns.com" _fcksavedurl="http://www.cricketondns.com">Cricket on DNS</a></li> <li><a href="http://www.youtube.com/user/InfobloxInc" _fcksavedurl="http://www.youtube.com/user/InfobloxInc">Infoblox YouTube Channel</a></li> </ul> <p>Technorati Tags: <a href="http://devcentral.f5.com/s/weblogs/psilva/psilva/psilva/archive/2011/05/09/" _fcksavedurl="http://devcentral.f5.com/s/weblogs/psilva/psilva/psilva/archive/2011/05/09/">F5</a>, <a href="http://technorati.com/tags/webinar" _fcksavedurl="http://technorati.com/tags/webinar">webinar</a>, <a href="http://technorati.com/tags/Pete+Silva" _fcksavedurl="http://technorati.com/tags/Pete+Silva">Pete Silva</a>, <a href="http://technorati.com/tags/security" _fcksavedurl="http://technorati.com/tags/security">security</a>, <a href="http://technorati.com/tag/business" _fcksavedurl="http://technorati.com/tag/business">business</a>, <a href="http://technorati.com/tag/education" _fcksavedurl="http://technorati.com/tag/education">education</a>, <a href="http://technorati.com/tag/technology" _fcksavedurl="http://technorati.com/tag/technology">technology</a>, <a href="http://technorati.com/tags/internet" _fcksavedurl="http://technorati.com/tags/internet">internet, </a><a href="http://technorati.com/tags/big-ip" _fcksavedurl="http://technorati.com/tags/big-ip">big-ip</a>, <a href="http://technorati.com/tag/dnssec" _fcksavedurl="http://technorati.com/tag/dnssec">dnssec</a>, <a href="http://technorati.com/tags/infoblox" _fcksavedurl="http://technorati.com/tags/infoblox">infoblox</a> <a href="http://technorati.com/tags/dns" _fcksavedurl="http://technorati.com/tags/dns">dns</a></p> <table border="0" cellspacing="0" cellpadding="2" width="378"><tbody> <tr> <td valign="top" width="200">Connect with Peter: </td> <td valign="top" width="176">Connect with F5: </td> </tr> <tr> <td valign="top" width="200"><a href="http://www.linkedin.com/pub/peter-silva/0/412/77a" _fcksavedurl="http://www.linkedin.com/pub/peter-silva/0/412/77a"><img style="border-bottom: 0px; 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The dynamic data center of the future, enabled by IT as a Service, is stateless. One of the core concepts associated with SOA – and one that failed to really take hold, unfortunately – was the ability to bind, i.e. invoke, a service at run-time. WSDL was designed to loosely couple services to clients, whether they were systems, applications or users, in a way that was dynamic. The information contained in the WSDL provided everything necessary to interface with a service on-demand without requiring hard-coded integration techniques used in the past. The theory was you’d find an appropriate service, hopefully in a registry (UDDI-based), grab the WSDL, set up the call, and then invoke the service. In this way, the service could “migrate” because its location and invocation specific meta-data was in the WSDL, not hard-coded in the client, and the client could “reconfigure”, as it were, on the fly. There are myriad reasons why this failed to really take hold (notably that IT culture inhibited the enforcement of a strong and consistent governance strategy) but the idea was and remains sound. The goal of a “stateless” architecture, as it were, remains a key characteristic of what is increasingly being called IT as a Service – or “private” cloud computing . TODAY: STATEFUL INFRASTRUCTURE ARCHITECTURE The reason the concept of a “stateless” infrastructure architecture is so vital to a successful IT as a Service initiative is the volatility inherent in both the application and network infrastructure needed to support such an agile ecosystem. IP addresses, often used to bypass the latency induced by resolution of host names at run-time from DNS calls, tightly couple systems together – including network services. Routing and layer 3 switching use IP addresses to create a virtual topology of the architecture and ensure the flow of data from one component to the next, based on policy or pre-determine routes as meets the needs of the IT organization. It is those policies that in many cases can be eliminated; replaced with a more service-oriented approach that provisions resources on-demand, in real-time. This eliminates the “state” of an application architecture by removing delivery dependencies on myriad policies hard-coded throughout the network. Policies are inexorably tied to configurations, which are the infrastructure equivalent of state in the infrastructure architecture. Because of the reliance on IP addresses imposed by the very nature of network and Internet architectural design, we’ll likely never reach full independence from IP addresses. But we can move closer to a “stateless” run-time infrastructure architecture inside the data center by considering those policies that can be eliminated and instead invoked at run-time. Not only would such an architecture remove the tight coupling between policies and infrastructure, but also between applications and the infrastructure tasked with delivering them. In this way, applications could more easily be migrated across environments, because they are not tightly bound to the networking and security policies deployed on infrastructure components across the data center. The pre-positioning of policies across the infrastructure requires codifying topological and architectural meta-data in a configuration. That configuration requires management; it requires resources on the infrastructure – storage and memory – while the device is active. It is an extra step in the operational process of deploying, migrating and generally managing an application. It is “state” and it can be reduced – though not eliminated – in such a way as to make the run-time environment, at least, stateless and thus more motile. TOMORROW: STATELESS INFRASTRUCTURE ARCHITECTURE What’s needed to move from a state-dependent infrastructure architecture to one that is more stateless is to start viewing infrastructure functions as services. Services can be invoked, they are loosely coupled, they are independent of solution and product. Much in the same way that stateless application architectures address the problems associated with persistence and impede real-time migration of applications across disparate environments, so too does stateless infrastructure architectures address the same issues inherent in policy-based networking – policy persistence. While standardized APIs and common meta-data models can alleviate much of the pain associated with migration of architectures between environments, they still assume the existence of specific types of components (unless, of course, a truly service-oriented model in which services, not product functions, are encapsulated). Such a model extends the coupling between components and in fact can “break” if said service does not exist. Conversely, a stateless architecture assumes nothing; it does not assume the existence of any specific component but merely indicates the need for a particular service as part of the application session flow that can be fulfilled by any appropriate infrastructure providing such a service. This allows the provider more flexibility as they can implement the service without exposing the underlying implementation – exactly as a service-oriented architecture intended. It further allows providers – and customers – to move fluidly between implementations without concern as only the service need exist. The difficulty is determining what services can be de-coupled from infrastructure components and invoked on-demand, at run-time. This is not just an application concern, it becomes an infrastructure component concern, as well, as each component in the flow might invoke an upstream – or downstream – service depending on the context of the request or response being processed. Assuming that such services exist and can be invoked dynamically through a component and implementation-agnostic mechanism, it is then possible to eliminate many of the pre-positioned, hard-coded policies across the infrastructure and instead invoke them dynamically. Doing so reduces the configuration management required to maintain such policies, as well as eliminating complexity in the provisioning process which must, necessarily, include policy configuration across the infrastructure in a well-established and integrated enterprise-class architecture. Assuming as well that providers have implemented support for similar services, one can begin to see the migratory issues are more easily redressed and the complications caused by needed to pre-provision services and address policy persistence during migration mostly eliminated. SERVICE-ORIENTED THINKING One way of accomplishing such a major transformation in the data center – from policy to service-oriented architecture – is to shift our thinking from functions to services. It is not necessarily efficient to simply transplant a software service-oriented approach to infrastructure because the demands on performance and aversion to latency makes a dynamic, run-time binding to services unappealing. It also requires a radical change in infrastructure architecture by adding the components and services necessary to support such a model – registries and the ability of infrastructure components to take advantage of them. An in-line, transparent invocation method for infrastructure services offers the same flexibility and motility for applications and infrastructure without imposing performance or additional dependency constraints on implementers. But to achieve a stateless infrastructure architectural model, one must first shift their thinking from functions to services and begin to visualize a data center in which application requests and responses communicate the need for particular downstream and upstream services with them, rather than completely in hard-coded policies stored in component configurations. It is unlikely that in the near-term we can completely eliminate the need for hard-coded configuration, we’re just no where near that level of dynamism and may never be. But for many services – particularly those associated with run-time delivery of applications, we can achieve the stateless architecture necessary to realize a more mobile and dynamic data center. Now Witness the Power of this Fully Operational Feedback Loop Cloud is the How not the What Challenging the Firewall Data Center Dogma Cloud-Tiered Architectural Models are Bad Except When They Aren’t Cloud Chemistry 101 You Can’t Have IT as a Service Until IT Has Infrastructure as a Service Let’s Face It: PaaS is Just SOA for Platforms Without the Baggage The New Distribution of The 3-Tiered Architecture Changes EverythingThe Order of (Network) Operations
Thought those math rules you learned in 6 th grade were useless? Think again…some are more applicable to the architecture of your data center than you might think. Remember back when you were in the 6 th grade, learning about the order of operations in math class? You might recall that you learned that the order in which mathematical operators were applied can have a significant impact on the result. That’s why we learned there’s an order of operations – a set of rules – that we need to follow in order to ensure that we always get the correct answer when performing mathematical equations. Rule 1: First perform any calculations inside parentheses. Rule 2: Next perform all multiplications and divisions, working from left to right. Rule 3: Lastly, perform all additions and subtractions, working from left to right. Similarly, the order in which network and application delivery operations are applied can dramatically impact the performance and efficiency of the delivery of applications – no matter where those applications reside.
