In the previous post Dustin shared a great video demonstrating the value of the IBM Image Construction and Composition Tool that is now delivered with IBM Workload Deployer V3.1. This is certainly one of the key new features of IBM Workload Deployer V3.1. However, there are also a number of other compelling enhancements and features that we would like communicate.
I created the attached video to highlight some of these features included in new Workload Deployer release. The video uses the web console to highlight some of the features and capabilities, giving a brief introduction for each one. Without going into a lot of depth, I think it gives a nice overview. This may be especially helpful if you already have Workload Deployer v3.0 and want to see the value you will get when you upgrade to Workload Deployer v3.1. Check it out.
We believe that these new features make IBM Workload Deployer V3.1 an even better solution for your private cloud needs. Please let us know what you think.
One of the things that often comes up at some point in IBM Workload Deployer conversations is the notion of self-service access. Specifically, users want to know what the appliance provides that enables them to allow various teams in their organization to directly deploy the middleware environments they need. In other words, they want to use IBM Workload Deployer to tear down the traditional barriers that exist between those that request the environment and those that fulfill said request. Now, as we begin to elaborate on this notion, it becomes quickly apparent that in order to effectively enable self-service, IBM Workload Deployer must deliver a few things.
First, IBM Workload Deployer must provide the means to define users with various levels of access. Second, IBM Workload Deployer must provide the means to define resource access at a fine-grained level to different users and groups of users. Check and check. The appliance has been doing this since the beginning of WebSphere CloudBurst. Without those two things, the conversation of self-service access would end pretty quickly. However, there is a final capability that is equally important: IBM Workload Deployer must deliver a means to limit resource consumption at a fine-grained level.
In IBM Workload Deployer there are a couple of ways to achieve this. First, you could define multiple cloud groups and allow access to those groups in a way that maps directly to resource entitlements. While that may work in some situations, others call for even more granularity. You may want to allow multiple different users or groups to access a cloud group, but you may want to allow different consumption limits for each of these groups. In this situation, you can take advantage of environment profiles and a new option when defining users of IBM Workload Deployer.
Consider the case that you have a group of developers and you want to limit their consumption of memory in the cloud. First, you start by defining your development users and for each you select Environment Profile Only as the value for the Deployment Options field.
By selecting the above value for the deployment options of a user, you restrict that user to only deploying via an environment profile as opposed to general cloud group deployments. After defining all of your development users, you may choose to organize them into a user group for easier management. At that point, you can define environment profiles and determine which ones your developers should have access to using the Access granted to field of the profile.
Within the environment profile, you can define resource consumption limits for compute resource and software licenses. For instance, you can define a limit on the amount of virtual memory consumed by all deployments using the profile. It is important to note that the limit is cumulative for ALL deployments that use the profile.
Now that all of the controls are in place, consider the deployment process for one of your development users. They pick a virtual system pattern, click the deploy icon and begin to configure the pattern for deployment. In the Choose Environment section of the deployment dialog, your development user will only be able to select the Choose profile option for deployment. Further, they will only be able to deploy using the environment profiles to which they have access.
After the deployment completes, a look at the Environment limits section in the profile shows the current usage totals.
Now suppose another development user, or even the same one, comes along and attempts to deploy another virtual system pattern even though the profile limits have already been reached. The user can initiate the deployment, but they will get a near immediate failure owing to the fact that they would exceed consumption limits if the deployment were allowed to proceed.
The same kind of enforcement occurs regardless of the resource limit type. You can use this approach to limit the consumption of CPU, virtual memory, storage, or software licenses among the various different users or groups of users you define in IBM Workload Deployer. If you combine fine-grained resource consumption limits with varying permissions and fine-grained access, I think you are on the road to truly enabling self-service in the enterprise.
If you are reading this blog then I am pretty sure that you are interested in the agility that can be achieved by rapidly provisioning middleware systems and standing up virtual applications in a private cloud environment. However there are other aspects of agility that you should also consider. One such aspect is the ability to build applications that can be easily maintained, updated, and extended. This is where OSGi technology comes into the picture.
If you have been working with the IBM Workload Deployer (or watching some IBM Workload Deployer demos) you may have noticed a category of components in the virtual application builder called OSGi Components.
Maybe you already know all about OSGi applications and the value they bring to an enterprise. Or, perhaps you noticed this and decided that you would search for some more information on this odd acronym and just what an OSGi application is all about.
In a nutshell OSGi technology is a way to define dynamic modules for Java. It provides a standard way to encapsulate components (called bundles) with metadata that define versioned package dependencies, service dependencies, packages exported, services exported, etc... basically everything you need to know about this bundle so that it can be connected up with other bundles to support a particular solution. These bundles can then be grouped together into applications and dynamically wired to fulfill necessary dependencies at runtime. The OSGi framework provides all of the necessary capability to manage the dependencies and resolve any problems.
Those who leverage OSGi technology benefit from improved time-to-market and reduced development costs. The loose coupling provided by the OSGi framework reduces maintenance costs and facilitates the dynamic delivery of components in a running system. Of course there's a lot more to it than just that ... involving portability across different environments, achieving the appropriate level of isolation or sharing within an environment, and integrating with the many different technologies and patterns already available today. I don't think I know enough about OSGi to do it justice here. But fortunately for me (and you) there are several experts who can make it all clear.
One such expert is Graham Charters and there is a great opportunity to hear him introduce this topic and also participate in a dialogue about the concepts and what they mean for your business. Graham will be leading a Global WebSphere Community Lab Chat on Wednesday of this week (July 20th) entitled: How can OSGi make your enterprise more agile. Graham is the IBM technical lead in the OSGi Alliance Enterprise Expert Group and an active participant in the open source community implementing many of these standards. So register now for this free session and learn how OSGi can make your enterprise even more agile.
A few weeks ago, I had a conversation with a current WebSphere customer about the potential value they could derive from the use of IBM Workload Deployer. Right away, this customer saw value in the consistency that a patterns-based approach could afford them. It was clear that patterns eliminate the uncertainty that can make its way into even the best-planned deployment processes. Initially though, the customer questioned the value of being able to do fast deployments because, in their words, "We don't deploy WebSphere environments that often." So, we continued our discussion, and then they asked an important question that I encourage all of our users to ask: "Why don't we deploy our WebSphere environments more frequently?"
It is interesting to talk with our WebSphere users that have a long history with our products. Often times, they have been taking a shared approach to WebSphere installations for many, many years. They develop innovative approaches and isolation schemes that allow them to carve up a single WebSphere installation (cell) amongst multiple different application teams. This allows them to avoid having to setup a cell for each application deployment and saves them the associated time. However, having talked to many different users taking this approach, it is not without its challenges.
As was the case in the customer I mention above, users typically made trade-offs when electing for larger, shared cells. As an example, if you have multiple different application teams with different types of applications using a single cell, applying fixes and upgrades to that cell can be a lot more complex. After all, you now have to coordinate plans across a number of different teams and find a window that fits all of their needs. For the same reason, trying incremental function via our feature packs is much more arduous in these types of cells. Additionally, administrative controls become more complex since teams with varying needs all require administrative access. Admittedly, this gets simpler with newer fine-grained security models in WebSphere Application Server v7 and v8, but it still requires organizational discipline and process.
At this point I should be clear that I am not denigrating the shared cell approach. It can work well, and we have many facilities built into the WebSphere Application Server product to support that model. However, if you are using this approach and you find yourself stumbling too much for your own liking, then I would strongly suggest that you explore the patterns-based approach of IBM Workload Deployer. By deploying patterns that represent your WebSphere cells using IBM Workload Deployer, you can quickly and consistently setup multiple WebSphere Application Server cells to support the varying needs of your application teams. You will still avoid spending an inordinate amount of time installing and configuring cells as that is an automated part of pattern deployment, and your application teams will still get the resources they need. Further, this can liberate your application teams in terms of how they apply maintenance, install upgrades, and absorb new function in the form of feature packs.
I am not suggesting a complete pendulum swing in your approach to how you manage multiple application environments. There is definitely a happy medium in terms of how many cells you end up with. After all, you do not want to trade in one set of problems for the problem of managing way too many different cells. However, I do think that decomposing monolithic, multi-purpose cells into smaller, more purposeful cells can be beneficial. In the course of thinking about this different approach, you may come to the same conclusion that the customer I mention above did. IBM Workload Deployer's rapid deployment capabilities are indeed valuable if you take a slightly different view of current processes.
In my opinion, declarative deployment models are key to the entire notion of Platform as a Service (PaaS). That is, users should concern themselves with what they want, but not necessarily how to get it. The PaaS system should be able to interpret imperatives from the user and automatically convert that to a running system. In this respect, I think the new virtual application pattern, and more specifically policies, in IBM Workload Deployer takes a giant leap toward a more declarative deployment model.
In IBM Workload Deployer, policies allow you to 'decorate' your virtual application pattern with functional and non-functional requirements. In other words, they provide a vehicle for you to tell the system what qualities of service you expect for your application environment. To put a little context around this discussion, let's examine the policies available in the virtual application pattern for web applications. Specifically, let's look at the four policy types you can attach to Enterprise Application, Web Application, and OSGI Application components in this pattern:
Scaling policy: When it comes to cloud, the first thing many folks think about is autonomic elasticity. Applications should scale up and down based on criteria defined by the user. Well, that is exactly what the scaling policy lets you do. You simply attach this policy to your application component, and then specify properties that define when to scale. First, you choose a scaling trigger from a list that includes application response time, CPU usage, JDBC connection wait time, and JDBC connection pool usage. After choosing your trigger, you decide the minimum and maximum number of application instances for your deployment, and then you choose the minimum number of seconds to wait for an add or remove action. At this point, you can deploy your application and IBM Workload Deployer will monitor the environment, automatically triggering scaling actions as needed.
JVM policy: I would be willing to bet that nearly all of you tune the JVM environment into which you deploy your applications. The JVM policy allows you to take two common tuning actions, setting the JVM heap sizes and passing in JVM arguments, as well as attach a debugger to the Java process (especially useful in development and test phases). You can also use the policy to enable verbose garbage collection (invaluable to understanding heap usage patterns for your application) and select the bit level (from 32 or 64) for your application. Again, all you have to do is attach the policy and specify the properties. IBM Workload Deployer will take care of the required configuration updates.
Routing policy: The routing policy provides a simple way to specify virtual hostnames and allowable protocols (HTTP or HTTPS) for your application. Attach the policy, provide the virtual hostname you want to use, select the desired protocols, and that's it! Remember, once you set the virtual hostname you will need to update your name server to map the hostname to the appropriate IP address.
Log policy: During the development and test phase, it is likely that you will want to enable certain trace strings in the application runtime. The log policy allows you to provide trace strings for your application, and it makes sure that the appropriate configuration updates occur in the deployed environment.
While this is not an exhaustive explanation of each of the policies above, I hope it gives you a basic idea of what they are and how to use them. To me, declarative deployment models are going to be a crucial part of making PaaS successful, so I am really excited about the notion of policies in IBM Workload Deployer. What do you think?
As I have mentioned before, IBM Workload Deployer v3.0 introduces choices in pattern-based deployment models. One of those models, virtual system patterns, is a carry over from the WebSphere CloudBurst Appliance. When you use virtual system patterns in IBM Workload Deployer, you can take advantage of all of the techniques you put to use in WebSphere CloudBurst. This is certainly good news for current WebSphere CloudBurst users, but it goes a bit further. Instead of simply maintaining the status quo with virtual system patterns, which would have been reasonable considering the introduction of virtual application patterns, we chose to continue to expand on your customization options for this pattern deployment model. In particular, I want to discuss three new features in IBM Workload Deployer that may help you to better construct and manage virtual system patterns.
The first new feature is one that I have been eagerly awaiting. In the new version of the appliance, we provide you with the ability to specify part and script package ordering in your pattern. This means that, within the virtual system pattern editor, you can tell IBM Workload Deployer in which order to start the virtual machines in your pattern, and you can specify in which order to invoke the script packages within the pattern during deployment. This eliminates the need for special script invocation orchestration logic in your pattern (I had customers resorting to a semaphore like approach using a shared file system), and it allows you to be more declarative about the virtual machine bring-up process. There are constraints, specifically with the part ordering. Some images will impose an implied part start-up order that you cannot change. For instance, deployment manager parts in the WebSphere Application Server Hypervisor Edition image must start before custom node parts. The good news is the pattern editor will not allow you to specify a part start-up order that violates these constraints. The image below shows an example of the ordering view in the virtual system pattern editor.
Another new feature that may influence the way you build virtual system patterns is the introduction of Add-Ons. You can think of Add-Ons as special script packages that you can include in your virtual system pattern that perform system-level configuration actions. Specifically, you can include add-ons in your virtual system pattern to add an operating system user, add a virtual disk, or add a NIC during the deployment process. You include Add-Ons in your pattern by simply dragging and dropping them onto a part in your pattern, just as you do with script packages today. The difference between script packages and Add-Ons is that IBM Workload Deployer will ensure the invocation of all Add-Ons before any other scripts run during deployment. We include default Add-On implementations for adding a user, disk, and NIC.
The last new feature I want to talk about today has more to do with how you manage or govern the deployment of virtual system patterns. In WebSphere CloudBurst 2.0, we introduced the idea of Environment Profiles as a way to extend your customization reach into the deployment process. Initially, these profiles gave you the ability to directly assign IP addresses to virtual machines in your deployment, declaratively specify virtual machine naming formats, and easily split a single pattern deployment across multiple cloud groups. In IBM Workload Deployer, you will be able to use these same profiles to set resource consumption limits for pattern deployments. In particular, you will be able to set cumulative limits for virtual CPU, memory, storage, and software licenses used by deployments tied to a specific profile, thereby giving you finer-grained control over cloud resource consumption. The picture below shows the new resource limit aspects of environment profiles.
Virtual system patterns are key in the deployment model choices for IBM Workload Deployer. Not only did we carry the concept over from WebSphere CloudBurst to IBM Workload Deployer, but we made it even better. Expect this trend to continue!
One of the fundamental tenants of IBM Workload Deployer is a choice of cloud deployment models. Starting in v3.0, users will be able to deploy to the cloud using virtual appliances (OVA files), virtual system patterns, or virtual application patterns. The ability to provision plain virtual appliances is a way to rapidly bring your own images, as they currently exist, into the provisioning realm of the appliance. As such, I think the use cases and basis for deciding to use this deployment model are fairly evident. However, when comparing the two patterns-based approaches, virtual system patterns and virtual application patterns, the decision requires a bit more scrutiny.
Our pattern approach is a good thing for you, the user. Basically, when we refer to patterns in the context of cloud, we are referring to the encapsulation of installation, configuration, and integration activities that make deploying and managing environments in a cloud much easier. Regardless of what kind of pattern you end up using, you benefit from treating a potentially complex middleware infrastructure environment or middleware application as a single atomic unit throughout its lifecycle (creation, deployment, and management). In turn, you benefit from decreased costs (administrative and operational) and increased agility via rapid, meaningful deployments of your environments. That said, it is imperative to understand the differences between virtual system and virtual application patterns, and more importantly, it is important to understand what those differences mean to you. Let's start by considering the admittedly simple 'Cloud Tradeoff' continuum below.
In the above graph, the X-axis represents the degree to which you have customization control over the resultant environment. The degree of control gets lower as we move from left to right. The left Y-axis represents total cost of ownership (TCO), which decreases as we move up the axis. The right Y-axis represents time to value, which similarly decreases as we go up the axis. Naturally, enterprises want to move up the Y-axis, but, and it can be quite a big but, they are sometimes hesitant to relinquish much control (move to the right on the X-axis) in order to do so. In that light, I think it helps to explore our two patterns-based approaches a bit more.
The most important thing to understand about this continuum is that the X-axis really represents the customization control ability from the point of view of the deployer and consumer of the environment. An example is probably the best way to explain. Let's consider a fairly simple web service application that we want to deploy to the cloud. If we were to use a virtual system pattern to achieve this, we would probably start by using parts from the WebSphere Application Server Hypervisor Edition image to layout our topology. We may have a deployment manager, two custom nodes, and a web server. After establishing the topology, we would add custom script packages to install the web service application and then configure any resources the application depended on. Users that wanted to deploy the virtual system pattern would access it, provide configuration details such as the WAS cell name, node names, virtual resource allocation, and custom script parameters, and then deploy. Once deployed, users could access the environment and middleware infrastructure as they always have. That means they could run administrative scripts, access the administrative console provided by the deployed middleware software, and any other thing one would normally do. The difference in using virtual system patterns is not necessarily the operational model for deployed environments (though IBM Workload Deployer makes some things, like patching environments, much easier). Instead, the difference is primarily in the delivery model for these environments.
Using a virtual application pattern to support the same web service application results in a markedly different experience from both a deployment and management standpoint. In using this approach, a user would start by selecting a suitable virtual application pattern based on the application type. This may be one shipped by IBM, such as the IBM Workload Deployer Pattern for Web Applications, or it may be one created by the user through the extensibility mechanisms built into the appliance. After selecting the appropriate pattern, a user would supply the web service application, define functional and non-functional requirements for the application via policies, and then deploy. The virtual application pattern and IBM Workload Deployer provide the knowledge necessary to install, configure, and integrate the middleware infrastructure and the application itself. Once deployed, a user manages the resultant application environment through a radically simplified lens provided by IBM Workload Deployer. It provides monitoring and ongoing management of the environment in a context appropriate for the application. This means that there are typically no administrative consoles (as in the case of the virtual application pattern IBM ships), and users can only alter well-defined facets of the environment. It is a substantial shift in the mindset of deploying and managing middleware applications.
Okay, with that explanation in the bag, let's revisit the diagram I inserted above. I hope it's clear that, all things being equal, virtual application patterns indeed provide the lowest TCO and shortest TTV because of the degree to which they encapsulate the steps involved in setting up complex middleware application environments. So, let's get back to my assertion that the customization control continuum really applies to the deployer and consumer. Why do I say that? It's simple. In the case of either the virtual system pattern or the virtual application pattern, the pattern composer has quite a bit of liberty in how they construct things. Sure, we enable you right out of the chute by shipping pre-built, pre-configured IBM Hypervisor Edition images, as well as pre-built virtual system and virtual application patterns. The key is though, that the IBM Workload Deployer's design and architecture also enables you to build your own patterns -- be they the virtual system or virtual application type. With anywhere from a little to a lot of work, you can build virtual system and virtual application patterns tailored to your use cases and needs.
At this point, you may be saying, "Well now you have really confused things! How am I supposed to decide what kind of patterns-based approach fits my needs?" I have some advice in that regard. First, map your needs to things that we enable with the assets you get right out of the box with IBM Workload Deployer. If your application fits into the functional scope of one of the virtual application patterns that we ship, use it. If you can support the application by using IBM Hypervisor Edition images, virtual system patterns, and custom scripts, do it. In this way, you benefit most from the value offered by IBM Workload Deployer. However, if you find that you cannot use any of the assets we provide right out of the box (e.g. you want to deploy your environment on software not offered in IBM Hypervisor Edition form or in a virtual application pattern), then ask yourself one simple question: "What do I want my user's experience to be?"
In this sense, I primarily mean a user to be a deployer or consumer of your patterns. You need to decide whether you favor the middleware infrastructure centric approach afforded by virtual system patterns, or if you prefer the application centric approach proffered by virtual application patterns. There is no way to answer this generically for all potential IBM Workload Deployer users. Instead, you have to look at your use case, understand what's available to help you accomplish that use case, and finally, decide on what you want your user's experience to be. I hope this helps!
Application-centric cloud computing is the main thrust behind the new capabilities of IBM Workload Deployer v3.0. But what does that really mean? After all, application-centricity is really just a concept. Granted, it is an important concept, but it is fairly meaningless until it is put into action or implemented. IBM Workload Deployer does just that with its new Virtual Application Patterns (VAPs).
VAPs are the embodiment of the workload pattern approach I briefly discussed in an overview post a few weeks back. The idea with a VAP is to give the user an interface through which they can provide their application, specify dependencies, declare functional and non-functional requirements and then deploy. Of course application middleware is a part of the overall solution, but IBM Workload Deployer has the smarts to build, configure, and integrate the necessary infrastructure in order to support the user's application. This is completely hidden from the user, so they are liberated to focus on the application and its requirements.
If we scratch a bit further beneath the surface of a VAP, we see that these patterns contain three primary pieces. These primary pieces are components, links, and policies, and they are fundamental to understanding how virtual application patterns work. Let's start with the building blocks of VAPs, components. Put simply, components represent different resources and functionality profiles that make up your application environment. As an example, the IBM Workload Deployer Pattern for Web Applications is a VAP that contains components for an EAR file, WAR file, message queue, and any number of other components that are typical requirements for a web application. The components will certainly vary based on the workload type (i.e. the components included in a web application VAP would be different than those included in a batch application VAP), but they are the foundation of any VAP.
From the ground up, the next logical element we come to in the VAP is a link. A link is a way to declare a dependency or integration point between two components. As an example, consider a VAP with a WAR file component and a database component. You might draw a link between the WAR component and the database component to indicate that your web application uses or otherwise depends on the database. IBM Workload Deployer interprets this link, and takes it as a directive to configure the integration between the two components as a part of deployment. In this case, that may mean configuring a data source in the application's container. This is just a simple example, and an application may have any number of links between components.
Finally, we come to the policy element within the VAP. A policy is a way for a user to specify functional and non-functional requirements for their application environment. Users attach policies to the VAP, or to components in their VAP, and IBM Workload Deployer interprets and enforces those policies. In the context of a web application, one example of a policy could be a scaling policy. The scaling policy might indicate scaling requirements for the application that included minimum application instances, maximum application instances, and conditions that triggered scaling activities. IBM Workload Deployer would use the information in a scaling policy within a VAP to appropriately manage the deployed, running environment. Other examples of a scaling policy may include a JVM policy that provides configuration directives for the java virtual machines in your application environment or a logging policy that defines logging configuration options. In any case, the policy element allows VAP builders to influence the configuration and management of the application environment.
In the example VAP below you can see the use of components (Enterprise Application, Database, User Registry, Messaging Service), links (blue lines between components), and policies (Scaling Policy, JVM Policy):
In total, when I look at a VAP a particular word sticks out to me: declarative. VAPs really enable declarative, application-centric cloud computing. What do I mean? By declarative, I mean you are telling IBM Workload Deployer what you want, but not necessarily how you want it done. It is the job of IBM Workload Deployer to take care of the how. This shift in approach to application environments enables the potential for significant savings, and more importantly to me, lays the foundation for a more agile, flexible approach to deploying and managing application environments.
There will be more in the weeks and months to come on IBM Workload Deployer, so stay tuned. I also want to put a plug in for a new blog from Jason McGee. For those that do not know Jason, he is an IBM Distinguished Engineer, and the lead architect behind IBM Workload Deployer. Be sure to check out his blog for insights on this new offering, as well as for all things cloud.
When one uses IBM Workload Deployer (previously WebSphere CloudBurst) to deploy a virtual system pattern, they benefit from a completely automated deployment process. The automation includes the creation and placement of virtual machines, injection of IP addresses, initiation of internal processes, and invocation of included scripts. Most of these processes are straightforward and require little more than a brief overview. However, the placement of virtual machines stands out, and it's inner workings are the subject of quite a few questions when I discuss the appliance. With that in mind, I thought I would provide a little more information on how the placement algorithm in IBM Workload Deployer works.
The placement subsystem in IBM Workload Deployer considers three primary elements: compute resource, availability, and license optimization. Compute resource availability is the gating factor for placement. That means that IBM Workload Deployer first looks at the available CPU, memory, and storage resource in the collection of hypervisors making up the cloud group(s) you are targeting for deployment. If a particular hypervisor cannot provide enough resource based on the amount you requested for your deployment, then it is automatically taken out of the eligible hosts pool. It is important to note that IBM Workload Deployer will overcommit CPU, and it will overcommit storage if you direct it to do so. It will not overcommit memory because that could severely degrade the performance of the application(s) running in the virtual machines.
After choosing the pool of hypervisors that are capable of hosting the virtual machines in your deployment from a compute resource perspective, the appliance then considers high availability. To better understand this particular placement stage, let's consider an example. Consider you are deploying a pattern based on WebSphere Application Server Hypervisor Edition and it contains two custom node parts. It is conceivable, and in fact likely, that these two custom node parts will host members of the same cluster, and thus the two nodes will support the same applications. As such, IBM Workload Deployer will attempt to place the two custom nodes on different physical machines in order to prevent a single point of failure. Of course, this depends on having two hypervisors with enough resource (CPU, memory, storage) to host the virtual machines, but the appliance makes that decision in the first placement stage.
After considering compute resource and high availability, IBM Workload Deployer moves to the last stage of placement: license optimization. In this stage, the placement subsystem attempts to place the virtual machines on hypervisors in a way that minimizes the licensing cost to you. The appliance can do this because it is aware of IBM virtualization licensing rules and takes those into account during this stage (if you aren't familiar with virtualization licensing rules and you are curious, ask you're sales representative to explain some time). During this stage, it will not violate any resource overcommit directives or rules in place, nor will it compromise system availability, but it will seek to minimize costs within these parameters.
At this point, I should make something clear that may already have occurred to you. You can override most of these placement rules by creating a cloud group containing only one hypervisor. In this case, IBM Workload Deployer will put all virtual machines on the single hypervisor until it runs out of compute resource (memory is likely to be the constraining factor). I would not suggest that you do this unless you have a good reason or you are in a simple pilot phase, but I do like to point out the art of the possible!
While not incredibly deep from a technical perspective, I do hope that this provided a few helpful details on what goes on during the placement stages of deployment. If you have any questions, do let me know.
Jason McGee will be leading the second GWC Lab Chat this week on Wednesday, 4/20. The very timely topic is related to recent announcements from IBM regarding the IBM Workload Deployer (see previous posts). Entitled "Application-Centric Cloud Computing" the discussion will focus on the concept of deploying and managing your application workloads in a shared, self-managed environment rather than manually creating and managing the application middleware topologies. It places the focus on the application rather than the infrastructure. This concept promises to deliver greater simplicity, elasticity, and
density among other things. It can position your business to react more
quickly and efficiently to the increasing demands of your customers and
free you from the managing all of the details.
Many of you may have already heard Jason speak last week at IMPACT 2011 in the cloud mini main tent or perhaps at any number of other sessions that Jason was involved in. Jason is the key architect behind IBM's WebSphere cloud activities. Obviously, Jason understands the cloud space very well and has a clear view of the evolution into Application-Centric Cloud Computing. This GWC Lab Chat will provide the opportunity to get your questions answered and share your perspective on this technology.
Jason will provide a brief introduction to the concepts and ideas and then lead an open discussion. Put it on your calendar and plan to attend - and please plan to bring your questions and comments to help foster a rich discussion. We want to hear from you.
If you haven't registered yet it is not too late - learn more and register here. It is easy to register and there is no cost. This is a very timely event and a great way to dig a little more deeply into concepts you first heard at IMPACT or perhaps hear them for the first time. Don't miss it!