CiRBA announced its upcoming release, Version 7.0, at VMWorld 2011 in Las Vegas two weeks ago. VMWorld is always an action-packed event with customer and partner get-togethers and the sheer volume of traffic through the conference (up to 19,000 from the 15,000 attendees last year), but truly the big highlight this year was seeing firsthand how people responded to Version 7.0. It was incredibly gratifying after months of hard labor by the entire team at CiRBA that the release was so positively received.

Even the TechTarget team running the VMWorld awards agreed. CiRBA Version 7.0 was a finalist in the New Technology category of “Best of VMWorld” 2011 awards.  Back in 2007, when we launched analytics for planning physical to virtual transformations, CiRBA won the Best of VMWorld Gold award for Capacity Planning & Consolidation Software, so you can imagine how proud we were to be recognized the second time out of the gate with a new solution – this time with analytics for gaining control over risk and efficiency in existing virtual and cloud infrastructure!

The New Technology category is open to any virtualization product that had not yet shipped but would be available by the end of 2011.  Version 7.0 will be shipping in Q4 this year. From the press release:

CiRBA DCI – Control, Version 7.0 of its analytics software, which provides a revolutionary new way of visualizing and managing environments, giving infrastructure managers unprecedented control over virtual and cloud infrastructure.

CiRBA DCI – Control’s intuitive new user interface, the CiRBA Control Console, enables infrastructure managers to determine in a single glance which resources are appropriately placed, configured and provisioned and which are at risk at the VM, host, cluster and environment level. Through the new Action System, CiRBA also automates the execution of recommended actions required to address identified risks and inefficiencies, with integrations to third-party management systems such as VMware®  vCenter^TM and VMware®  vCloud^TM Director. Together, CiRBA’s new Control Console and Action System greatly simplify and streamline infrastructure management processes by quickly communicating status and detailing precise actions required to optimize IT infrastructure and mitigate risk in accordance with an organization’s operational policies.

For a preview, check out a few videos on our website including an address by CTO and co-founder Andrew Hillier,   a demo of our new product, and a short video explaining how organizations can benefit from the solution.

If you can’t measure power consumption in the data center, you definitely can’t manage it. It’s no surprise that most companies are facing data center power constraints. And Virtualization and cloud computing have further complicated many aspects of power management. In the past, organizations were unable to accurately assess the true power consumption of individual servers, instead relying either on the plated power listings or using points of peak power as a reference. This practice has led to inaccuracy and under- or over-estimation of power requirements.

Here’s an article that discusses an Intel/CiRBA initiative to provide customers with a way to utilize actual time series server power to more effectively place workloads and manage power consumption across the data center. Tell us what you think. http://www.citoresearch.com/content/power-management-data-center-capacity-control

A recent CiRBA survey of 94 individuals from large enterprises found that only seventeen percent of organizations had achieved their density and ROI goals with virtualization. In addition, seventy percent of the respondents indicated that they planned to move to cloud operating models from existing virtual environments in order to achieve cost savings.

That all makes sense, if you believe that cloud operating models will deliver cost savings. Research completed by CiRBA CTO Andrew Hillier last year showed that costs in external clouds add up quickly and in many cases, you are better off from a cost perspective to leverage internal infrastructure.

You might say then, lets look at filling up our internal cloud first. But internal clouds by their very nature can increase costs. Users with self-serve access to capacity more often than not act like diners at an all you can eat buffet, over-indulging in capacity. Often this is due to the desire to safe-guard against risk or simple lack of knowledge as to what is really required to service the workload. Pre-defined instance configurations and sized “buckets” of capacity may simplify management, but can also result in built-in excess capacity vs. custom allocations for each workload’s true requirement.

The biggest challenge in saving money with internal clouds however, lies in the challenge of increasing density over existing virtual environments. Internal clouds hold the promise of increased density as a result of sharing infrastructure across a broader base of users. This is a rational belief, but in practical terms you can only achieve higher utilization if you actually know how to increase density without putting workloads and performance at risk. Examining how successful organizations have been at managing utilization levels within purely virtual infrastructure would suggest they don’t.

Gartner analyst David Cappuccio recently commented in CIO that utilization in virtual infrastructure is stuck at 25% for most organizations. “Easily more than half of the clients we talk with have this situation. In fact, utilization numbers should be way higher, up around 55 to 60 percent, to gain the true economies of running virtualized applications…” he explains. So why do organizations expect to save money in the cloud, when their existing virtual infrastructure is potentially under-utilized?

Much of the focus always ends up on sizing workloads properly. Sizing is critical. Mapping workloads to the right-sized cloud instance ensures performance and the most efficient use of capacity. This requires analysis of the workload utilization profile and personality while factoring in service level requirements, operational requirements to find the best match within the instance catalog. Having an established approach and process to “sizing” and “matching” not only enables you to minimize waste, but it gives you ammunition to combat the buffet style capacity binge and show application owners why a particular instance option is best for their workload.

There is another critical factor that is often neglected. That is the impact of workload placements on how well utilized infrastructure is. If you want to maximize density, you need to strategically place workloads together on infrastructure. According to Gartner analyst Alessandro Perilli, in the June 9, 2011 research paper “The Big Mind Shift: Capacity Management for Virtual and Cloud Infrastructures,:

“Gartner defines “optimized” as a virtual infrastructure where the workload placement satisfies all of an organization’s technical, business, and compliance constraints and the capacity is allocated to avoid resource wasting (i.e., rightsized).”

If you think of it like a game of Tetris, it’s easier to see how placement is critical to making the best possible use of your infrastructure. If you fit the workloads together well considering their size (workload personalities and patterns), shape (policies and requirements that apply), and available space (capacity) then you can maximize use of the available capacity. A poorly played game of Tetris leaves a lot of empty space in the playing area and in the context of infrastructure this means wasted capacity. Things get even trickier when you have to factor in all the applicable policies that dictate where workload can go such as required service levels, privacy, security, operational and management, etc. to determine the best placements.

The reality is that sizing and placements are challenges in establishing both virtual and cloud infrastructure. Getting it right isn’t a new problem, but organizations migrating virtual infrastructure to hybrid clouds aren’t going achieve the big upfront savings on hardware that they realized with virtualization. Saving money with the cloud is going to be much harder and making good infrastructure choices will only get you so far. The real savings will only be won by organizations that figure out how to effectively plan and manage workload placements and infrastructure allocations so that policy requirements are met without giving up on efficiency.

I’ll throw it out there – raw utilization measures are useless in determining the efficiency or effectiveness of a virtualization initiative or the ongoing management of virtualized infrastructure.

As long as we have had distributed computing there has been an interest in CPU, Memory and IO utilization.  In a largely physical world, these measures were fundamentals of the capacity management and performance tuning disciplines.  Understanding utilization levels told much of how an application might be performing, where there might be issues or how long a given server would be sufficient to handle a particular workload.  Of course, much of the server sprawl of the 90’s and early 2000’s was created by the need to service many workloads on an individual basis.  The phrase one app / one box was accurate.  Capacity managers still had to scrutinize the stats on larger mission critical apps, but the average workload was left to take a small percentage of the capacity of the server that it sat on.

Virtualization technologies have driven the revolution of consolidation and more efficient use of server infrastructure.  The dream is to have greater utilization on a smaller number of servers.  It follows logically then to assess how well you are doing in that goal by looking at server utilization post virtualization – maybe even set goals around it?  The term capacity management sounds the same but the discipline is now very different.  Rather than using analytics to determine how to optimize performance or solve a problem on a single, important application, the challenge is now much broader – potentially dealing with the efficient uses of capacity for thousands of workloads on hundreds of hosts.

A recent survey conducted by of 100 large enterprises showed that 70 percent of companies are using raw utilization measures, in particular CPU utilization and memory utilization, to determine efficiency in virtual infrastructure.  So while the challenge has changed, organizations still rely on the same approaches and measures to determine requirements and “how well they are doing.”  Raw
utilization might be a useful metric to compare on average how well-utilized infrastructure is from a before and after perspective when moving from physical to virtual – but even then it’s emotionally satisfying but practically useless.

The first question to ask is “what is the optimal target?”  Of course that will vary depending on the type of applications, the service level requirements, workload personalities, HA and DR strategies and the list goes on.  There is no one answer that fits across the board.  The second question, and perhaps most impactful, is “which constraints dictate which workloads can go where?”  In environments that are horizontally scaled like web farms, this is less of an issue, but the majority of applications that support a business are bound by multiple constraints relating to compliance, physical location, maintenance windows, political lines, legal jurisdictions and many, many others.  When these constraints are factored in, raw utilization goes out the window.  Instead, looking at infrastructure requirements and utilization when burdened by constraints gives a clear picture as to the number of servers or how much capacity you actually require, and how much, given those considerations, you are actually using.   A “fully-burdened” measure may result in servers that only ever see five percent of their capacity used and that may be as good as it’s going to get based on the constraints at play.  Does that mean that you are doing poorly from an efficiency perspective?  No!  It means that to be compliant with the constraints at play, you need to run servers at that level.

What’s the point of measuring the impact of all the constraints and considerations accurately?  Without these measures, capacity requirements are left to guess work.  My own team’s experience in the field shows that IT staffs guess high to avoid risk. In most cases, they guess very high. So while you think you have 20 percent allocated for growth and risk protection, you might actually have upwards of 100 percent. This is great from a risk avoidance perspective, but it goes without saying that the larger the environment, the greater the waste and cost.  Including all the policies and constraints into your efficiency measures and capacity requirement determination is critical to be able to reduce infrastructure costs in virtual and internal cloud environments. The second critical step is to optimize workload placements according to these same considerations in order to make the best possible use of infrastructure.

The most efficient well-managed environments we have found in the field understand the impact of policies and workload requirements and incorporate those into both the capacity and workload placement decisions. These organizations look at environments both from a macro perspective while also ensuring that the individual workloads and hosts are well cared for and aligned.  It’s a shift in perspective and approach, but one that pays off handsomely in the end.

Most organizations rely on utilization to measure efficiency in virtual & cloud infrastructure,
but should they?

It is no surprise that the vast majority of global organizations are looking to incorporate cloud models into their IT strategy in 2011.  There are lots of studies out there that point to why clouds are so appealing. The top reasons uncovered by our own survey included cost savings, greater flexibility for infrastructure provisioning, easing management pain through standardization increased responsiveness, and shorter provisioning times.

As enterprises dig deeper into cloud operating models, they realize that achieving IT nirvana and actually migrating to the cloud is tougher than it first seemed.  The easy part is setting up a self-service portal to enable users to request capacity.  Much more difficult to tackle are establishing processes for managing the workflow of inbound requests, figuring out what existing infrastructure should migrate over, how to handle that process and do it in a reasonable timeframe while ensuring that the aforementioned goals are met. Add in the growing list of infrastructure options to sort through, business and operational policies, security concerns, service levels and internal politics about cloud infrastructure and a migration initiative can turn into a veritable rat’s nest.

Top performing organizations have recognized that poor decisions lead to inefficient, risk-laden environments that fall short of everyone’s expectations. Often they have experimented with spreadsheets or vendor supplied tools to determine sizing and other basic processes to determine configurations to enable standardization. Once they grasp the complexity of the task, realize the challenge in front of them and just how time intensive and error-prone manual approaches can be, they start searching for a better way.

Key to success is investing in a process that supports a methodical, consistent way of analyzing workloads , applying pre-defined, documented policies to decisions, and placing workloads appropriately – whether in a cloud or somewhere better suited.  This kind of process not only enables consistency in decision making, but applies a discipline that is required to ensure that decisions are based on all the critical criteria and that a “cloud first” policy is applied only where and when it makes sense. One of the organizations we have worked aptly called this process “The Cloud Factory”. Truly fitting considering the fact that they were processing 100,000 workloads!  A good “Cloud Factory” process involves:

1. Qualifying candidates for suitability for virtualization and cloud environments against admission policies, sifting out those that don’t belong.
2. Sizing qualified workloads, comparing them against the available instance catalog(s) and choosing the best environment and instance options based on fit, service level requirements, and cost.
3. Mapping candidates to optimal OS and SW stacks to enable best fit and standardization.
4. Analyzing workloads into target environments.
5. Dealing with exceptions when workloads don’t fit or aren’t suitable to place into clouds.
6. Monitoring internal environments leveraging a fuel gauge to understand how much room remains for additional workloads and ensuring that over time, instance containers continue to be appropriately sized and workloads appropriately placed.

Consistent application of qualification criteria, utilization analysis, operational and business policies can be challenging and is why many organizations skipped some steps when planning virtualization. They simply weren’t aware of a better way to do it and the challenges of compiling the necessary data, conducting the analysis and coming up with an answer with any kind of accuracy and in a reasonable timeframe were simply too great to overcome.  Applying purpose-built analytics to the problem streamlines these processes while standardizing analysis approaches for smarter, faster decisions in the end.

As our friend James Staten of Forrester says: “Success with your internal cloud won’t come simply because you build it.”  Only by methodically analyzing the workload demands against the resource supply, and meticulously managing the placements of cloud instances and the resources allocated to them, can clouds strategies pay off, and ultimately provide a level of agility that will truly transform the way IT resources are managed.