V mware v fabric 5 - what's new technical sales training presentation

Editor's Notes

• #4: To address this challenge, VMware is focused on three core solution areas in IT: How best to evolve the infrastructure to support this new world, Changes in application development to speed time-to-market for business-critical applications, that take advantage of this new world, And a new way of approaching end-user computing, to increase user satisfaction. <click> Let’s begin by exploring the evolution of infrastructure.
• #6: Rails = framework for Ruby Django = framework for Python
• #7: Point to emphasize: in the past, middleware has run on physical servers. Today, it’s increasingly shifted to running on VMs. And that brings about a compelling opportunity for VMware and its partners.
• #12: Note: a la carte SKUs are still available to sell.
• #13: Q. How is vFabric 5 Engineered for vSphere?   Optimizations for vSphere include: Elastic Memory for Java (EM4J), which allows for dramatically higher  consolidation ratios for Java applications running on vFabric. EM4J pools Java heap memory across tc Server instances running on the same vSphere host, enabling a tc Server that is under memory pressure to borrow Java heap memory from its neighbors. This enables application teams to size Java heaps according to typical workloads, not peak workloads, which in turn lets them create VMs with smaller memory footprints, and pack more tc Server instances onto a single vSphere host. It also reduces the likelihood of out of memory errors that can cripple applications. Java Best Practice Analyzer, a vSphere client plug-in which enables vSphere administrators to view and controls settings on the vSphere virtual machine, guest operating system, and Java virtual machine (JVM) that can impact Java application performance. Java Best Practice Analyzer identifies common configuration and deployment problems that can cause slow Java performance on vSphere.   Q. How is vFabric 5 Engineered for Spring?   Optimizations for the Spring Framework include highlights such as Spring Insight Operations  which allows unparalleled visibility into the performance of Spring applications. Spring Insight Operations features: Easy, automatic instrumentation of Spring applications running on tc Server to enable app operations teams to view application performance both at overview and fine-grained perspectives. Isolation of performance problems by application, endpoint, application server, and method. Tracking of performance over time, to understand whether problems are chronic or spurious. Response time histograms, to understand whether performance problems affect a large portion of users, or a small subset. Highly performant instrumentation with sub microsecond impact to application response time, allowing it to be run on applications in production Hyperic integration, enabling allows the Operations and Development teams to view a single set of both server-oriented and application oriented performance, and collaborate more effectively to fix performance problems faster. Metrics from Spring Insight provide Hyperic with a broader range of data for triggering alerts for sysadmin notification, and control actions for automatic problem remediation. Parallel deployment is explained at http://www.tomcatexpert.com/blog/2011/05/31/parallel-deployment-tomcat-7. From that blog post: Upgrading web applications can be very expensive if your storefront is the web. Weekend maintenance windows, or downtime in general can give an entire company heartburn. Survey data shows that web application downtime can cost some companies up to $72,000. Yet the cost of not constantly rolling out new features and bug fixes can equally penalize a company in the competitive online markets today. Previously, to upgrade an application on Tomcat and avoid downtime, system administrators would have to set up multiple instances of Tomcat and do some very clever stuff with load balancers. This equals extra hardware costs as a permanent part of the company’s infrastructure. Now with the advent of parallel deployment in Tomcat 7, you can have multiple versions of the same application installed at the same time on a single server. Users with active sessions can continue to use the old application and new users will be routed to the new version. This way, no user sessions will be interrupted, and the old application can gracefully phase out. Setting Up Parallel Deployment Parallel deployment is a function of the Context Container. The Context element represents a web application, which in turn specifies the context path to a particular Web Application Archive (WAR) file that is the application logic. Parallel deployment allows you to deploy multiple versions of a web application with the same context path concurrently. When choosing what version of the application for any given request, Tomcat will: Route new requests to the latest version, so new sessions go to the new application. If session information is in the request, check the session manager for a matching version, so existing sessions will go to the original application for the request. If session information is in the request, but the corresponding application is no longer present, it will route to the latest version. Working Across Clusters If you have clustered Tomcat, configured using the FarmWarDeployer, you can achieve parallel deployment within that cluster. The FarmWarDeployer is a component which lets you deploy a WAR file across an entire cluster. By placing the WAR file on the designated host, clustered Tomcat will automatically replicate that deployment across every server in the cluster. Footnote Because of the sophistication and cost savings this feature brings to enterprise deployments of Tomcat, this feature, built by VMware’s SpringSource, was initially looked at being part of VMware vFabric's tc Server, but once we got building it, it couldn’t be done cleanly without putting some hooks into Tomcat that didn’t fit naturally into the architecture. After careful consideration of what was best for Apache Tomcat, VMware decided to still fill the need to build this feature out, but donate it to the Apache Tomcat project at large.
• #17: Use Cases 3 and 4 are where GemFire excels, and is where we start to get well beyond simple caching technologies.  There are really only a couple of products that address this in an enterprise manner: GemFire and Oracle Coherence.   Use Case 3: Customer is creating a true data grid: lots of clients, connecting to many nodes, some of which may cross WAN’s The data is kept in sync across all nodes at all times and so is highly available.  Data can be persisted using lazy writes in order to achieve database-like reliability The data grid can be used to publish and subscribe messages across topologies – and so for many use cases acts as the message broker as well as the data store – eliminating the need for a messaging layer altogether There are typically many clients: one of the scenarios we often see is where the clients are desktops, executing macros and pulling real time data from the data grid.  This is in fact a significant mostly what the financial services customers are doing on Wall Street: pretty much every bank is using GemFire today (primarily for their trading apps) The clients are pulling data from the grid and executing some kind of logic on the data – similar to a typical messaging use case with a message broker, except much faster because the data is always local to the app that needs it.  In this way you get huge improvements in throughput   Scenario 4 The primary difference when compared to #4 is that the execution is actually being done IN THE DATA LAYER.  Both GemFire and Coherence have the ability to execute code within the process – and so allowing for an even greater throughput when compared to scenario #3.  In this case it somewhat blurs the line between the typical 3 tier architecture…and so is a pretty high-end use case.  However that being said, it is in fact what high end fin services customers are doing with the product. The end result is really about faster throughput.  I would guess that this would be of interest to Reuters given what they do.  I believe that they use Tibco for messaging (at one time they were an investor in Tibco if I recall correctly) and so they may have strong architectural opinions about how to achieve scale for messaging, but at the data layer they would need something like GemFire.
• #19: Session replication and L2 cache without turning app servers into stateful servers. C/S is essentially all the benefits of a database without the drawbacks. Database capabilities in-memory Access to the more sophisticated GemFire features - data-aware function routing,
• #22: Spring Framework 3.1 introduces a caching abstraction via the @Cacheable annotation for incorporating pluggable caching strategies into applications. The @Cacheable annotation uses Spring AOP and AspectJ under the covers. The following swappable distributed caches are built in: Ehcache, Coherence, GigaSpaces, and GemFire
• #26: For cluster status and performance monitoring, use GemFire tools Hyperic can manage JMX Agent Gateway Hub 6.5 Cache Server 6.5 Application Peer 6.5
• #32: Recommended Forrester Reading: Lean Software Is Agile, Fit-To-Purpose, And Efficient; Dec 12 2008 Lean: The New Business Technology Imperative, Sep 29 2009
• #34: Although Spring is famous for its openness and portability, we can take advantage of our deep understanding of Spring to add unique value for applications running on the vFabric platform. Spring Insight is a technology that provides code level tracing – tracking the time a transaction spends in each method and JDBC (database) call – transparently for Spring Applications. Basically, users get very deep information on the operation of a running Spring application just as a benefit for running in on vFabric. No code modifications or setup is required. Spring Insight has been available for developers for almost a year as part of the tc Server Developer Edition. This is a single-user, development-time solution that integrates helps developers find performance bottlenecks in their applications while running it on their local machines, typically inside our Eclipse-based IDE, SpringSource Tool Suite. Next year we will roll out Spring Insight Enterprise, which leverages the same code-level tracing technology but pulls together information from multiple application servers into a single console with roll-up views, drill downs, and historical comparisons. This solution has been designed to minimize the overhead of the monitoring so it is suitable for both test and production environments. While the Spring Insight Enterprise console is useful for both administrators and developers who may need to troubleshoot problems in production, it is also important that administrators and operations have a single place to go for vFabric monitoring. Therefore, Spring Insight Enterprise monitoring will be available not only as a stand-alone console but also through a plug-in to the Hyperic monitoring solution, enabling a single view across data from multiple sources and integrated alerting and control actions.
• #35: Over the past year, we have invested a lot of effort in providing automatic visibility into Spring applications so that our customers can answer important questions about the behavior of their applications. In this particular example, we have a Hotel Booking application. By using the Spring Insight technology baked into our application platform, our customers can interact with a chart that illustrates how their application is performing and whether or not the response times are within the expected SLAs. If there is an issue, they are able to drill into the application further. Let’s say that certain response times for finding and displaying available hotel rooms look out of band…well, you can selectively drill into that request and see if the database is the bottleneck, for example. This level of visibility helps developers create high performing applications, but as we will cover in a moment, this information combined with visibility across the application infrastructure stack is also critical for delivering the proactive performance management required of today’s modern platforms. 1. Optimized for Spring applications and tc Server. Easily installation and automatic instrumentation for Spring applications running on tc Server, in-depth tracing through endpoints in complex transactions. 2. Multi-user, multi-application, and multi-node clusters - UI is designed to view aggregate and individual metrics and traces by applications, servers, and end points. 3. Highly performant instrumentation with sub microsecond impact to application response time, allowing it to be run on applications in production (I would like to use this to lead the performance impact discussion). 4. Easily customizable thresholds for end points to have a more accurate reporting of apdex score and overall application health. 5. The Hyperic integration allows the Operations and Development teams work more closely together on actual production data. The addition of Spring Insight data makes it easy for an operations person to have quicker root cause identification and involve the developers earlier in a more timely manner. Hyperic adds to Insight the ability to alert on and correlate its data to Hyperic's application infrastructure resources and metrics. -- New features in Spring Insight 1.5.1 over 1.0 - Production agent/dashboard deployment is the big feature ( the 1.0 release was developer-only ) - Updated to spring 3.0/tomcat 7 - Data persistence - 1.0 didn't save data to disk - Much better UI over 1.0 - Ability to mark certain code paths as "sensitive" to prevent them from being sent to the dashboard. - GoogleSpeedTracer was emphasized in the 1.0 release, it still works, but we have emphasized it less. Worth a mention.
• #36: One of the most common challenges facing anyone running a Java application is figuring out how much memory to give it. Since Java heaps are static, giving too much memory can be very wasteful, and can sometimes even lead to poor performance through long GC (garbage collection) pauses. Conversely, if a JVM does not have enough memory the application will encounter an out-of-memory exception and simply crash. It would be very useful to have a way to give a little extra memory to a JVM with the understanding that, when it is not needed, the memory will be reclaimed into a common pool. But if the Java application encounters a spike, its full memory assignment will be available to it. Of course, ESX already has ways to reclaim and then give back memory to virtual machines at the OS level, so what we need here is an extension of that technology that operates at the Java level. The vFabric platform will be offering exactly that capability, working with some new capabilities to be introduced in ESX. We call the technology Elastic Memory for Java or EM4J. Elastic Memory for Java is an agent that runs directly inside Java and manages memory within the Java heap. When Java is lightly loaded, the agent allocates objects onto the heap, creating a “balloon”. The memory associated with these objects is then passed to ESX, which is then free to give this memory to other virtual machines that need memory more urgently. When Java is under heavy load, the balloon is deflated and Java has all the memory it needs to handle heavy load.
• #37: When running a vFabric application on top of vSphere, it is natural to ask: how do I make sure that I am following best practice? New technologies like EM4J promise to make the applications run even better than before on vSphere. But again: how do I make sure that I understand what EM4J is doing and make sure that everything is set up right? The trickiest part of following best practice for Java applications is that, while some of the required configuration is at the vSphere or VM level, other items require insight into Java configuration. So how can a vSphere admin know that things are set up right – and that they stay that way? The vFabric Java Best Practice Analyzer is a vSphere client plug-in that gives vSphere admins a simple interface to check that both the Java-level and VM-level best practices are being followed. And if they aren’t, tell them exactly what to do about it. In addition, the Best Practice Analyzer gives vSphere admins a view into, and control over, the operation of EM4J. Note that while the initial focus of the best practice analyzer will be on the vFabric Java runtime container, tc Server, it will expand over time to support other application servers.
• #38: What kind of benefits can be had from such a platform? This is an example from one of our large Financial Services customers with very large Java deployments. They are comparing their existing WebSphere-based infrastructure with our modern, lightweight alternative. Operational savings for such things as server provisioning and application installs can be driven down from hours/days to seconds/minutes. The complexity of application infrastructure can be dramatically reduced as well. With Gigabyte footprints reduced to Megabytes and corresponding installation and configuration scripts reduced by orders of magnitude. These are just some examples, from the customer’s perspective, of the measurable benefits.
• #43: Design guidance from http://redis.io/ Note that “easy to use” isn’t in this copy. That’s because it’s implied with the tagline “messaging that just works” and the extensive list of tutorial links.
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• #48: The cold reality is that a typical datacenter will have a wide range of technologies, making effective app management extremely difficult. Can you manage everything in your datacenter? Will you be able to manage technologies that will be acquired in the next 12 months? Are you sure your monitoring is future-proof? Hyperic can monitor all this and more. Plus its community is constantly building support for new servers, based on Hyperic’s easily extensible plug-in development kit. More information: http://www.hyperic.com/products/supported-technologies To view the metrics that Hyperic supports: Log into Hyperic Administration tab Click “Monitoring Defaults” Select the technology of interest Click “Edit Metric Template”
• #55: As we think about it from an enterprise perspective, Cloud Foundry is an opportunity to boost application velocity through systemic productivity improvements across the entire dev and ops pipeline. Developers can focus on the app while IT delivers the platform as a service. PaaS speeds up prototyping, trial, testing, scale testing, production, updating, cloud-scaling apps.
• #58: Add: cloud foundry inside vFabric Target destination of cf.com Add other providers running cloud foundry Add other languages…ruby
• #62: Developers and IT get curated, updated and operated applications infrastructure need not be upgraded or patched