Turbo Talk

Our CEO Shmuel Kliger will be a panelist at CloudConnect (March 15-18 in Santa Clara) where he’ll be participating in a discussion on Cloud Economics and new pricing models, moderated by Cloud Economics guru and AT&T VP Joe Weinman.

Here’s the description of the panel from the CloudConnect site:

Cloud ROI just entered a new phase with the introduction of spot pricing.  But wait; there’s more!  This panel of leaders will address cloud pricing and market system evolution and what it will mean for ROI.  Buyer’s councils, intercloud economics, new cloud resource allocation systems that use bidding and auctions to determine resources to invoke will all be part of this emerging cloudscape.

If you’re planning on attending, stop by and say hello.

Shmuel would love to chat with you, especially if you are a long suffering Jets or Mets fan!

Over the last few days, there have been a couple of interesting developments and posts from the cloud community that have me thinking about the economic ramifications of cloud computing.

Amazon just announced spot pricing for EC2 instances:  Amazon users can now name their price for an EC2 instance and if capacity is available at that price, the instance will be purchased.  Werner Vogels (Amazon CTO) discusses this in more detail on his blog.

Hedging Your Options for the Cloud:  In this GigaOM post, Joe Weinman of AT&T discusses a variety of economic and business models for cloud computing.

Taking pricing models used in other industries (e.g. airlines, hotels, manufacturing, internet advertising) and applying them to the management of clouds makes sense – especially as compute power and application capacity becomes commoditized.  However, there is still much work to be done to achieve the same level of pricing and service granularity in the application world.

For instance, a unit of cloud CPU power or cloud storage is somewhat fungible, and it is fairly clear what you’re purchasing for your money.  On the other hand, trying to appropriately value, sell, or trade a unit of specific application capacity running within an enterprise is not.

For these new pricing models to have the most impact and business value to end users of IT, they’ll need to be viable in the cloud as well within the four walls of the enterprise.

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• New From Amazon: Spot Pricing on Cloud Computing (datacenterknowledge.com)
• Amazon Slashes the Price of Reserved Instances (techcrunchit.com)

Robert Grossman recently blogged about the need for the definition of a common deployment unit of cloud computing technology.   As he rightfully points out in his article, there are numerous layers of technology, all of which could lay claim to being the home of the unit of record.

Although the definition of the “right” deployment unit of measure is an important one, I’d also say that the unit of performance or efficacy (pertaining to virtualized IT and cloud computing environments) is also important.

Here are some examples of virtualization efficacy metrics that keep coming up in our discussions with virtualization/cloud admins and architects:

- OPEX reduction
- CAPEX avoidance
- Consolidation ratios
- # of servers migrated from physical to virtual
- datacenter rack/floor space reclaimed

These metrics resonate with CIOs, IT managers focused on server virtualization, and server administrators, and in many cases their job performance is measured relative to these metrics.  What we’re finding is that because these metrics do not translate well to other silos of the IT stack, virtualization projects (particularly ones that occur after the initial consolidation project) can get delayed or stopped because its hard for the other siloes to see “WIFM“, with metrics that matter to them.

What are the metrics that matter to you?  And what metrics do you use to sell the benefits of virtualization and cloud to the other silos of your IT organization?  We’d love to hear your thoughts on this topic.

In the last two decades IT management is in a race to nowhere. A race to discover more, collect more and present more. More reports, more graphs, more views. If there is an IT asset out there, there is a tool to discover it. If there is a metric that can be collected, there is a tool to monitor and graph it. If there is a knob to turn, there is a tool to control it. How many management tools do you have in your environment? Which one do you use when? How many different reports/views are you looking at each day? How much time do you spend investigating these reports? Are you in control? Do you sleep better?

IT environments are complex. Managing these environments can be challenging. Many different moving parts with very complex interactions make it very difficult to effectively track, monitor and control the environments. For years, trying to address the broad range of pain points, we kept throwing more and more management tools into the environment. Every pain point we answered with a different tool. Introducing a new technology or product to the environment led to the deployment of yet another management tool. Very quickly we ended up utilizing hundreds of different management tools/products. Instead of addressing the management challenges, we increased the TCO and created an operational and administrative nightmare. In fact, many enterprises have embarked on multimillion dollar projects to reduce the the number of management tools in use from several hundred to an end state of (hopefully) a few hundred.

The IT landscape is transforming. Virtualization, SaaS and cloud lead to increasingly more dynamic, complex, heterogeneous and large IT environments. In the same time, the constraints within which IT needs to operate are becoming more and more stringent. On one end, business constraints and regulation require strong alignment of IT with the business to meet aggressive business and service goals while keeping OPEX and CAPEX under control. On the other end, environmental constraints of energy, space, etc. limit the flexibility and the free ride we used to have.

We are no longer looking at static siloed IT environments where the boundaries between the technology layers and products were well defined and understood. Virtualization brings these walls down. No longer are we managing one application on one known dedicated physical machine and attached storage, but rather a complex dynamic boundary-less compute capacity available for our applications on-demand. Can we scale to these types of environments with the existing brittle intelligence tools and approaches?

In spite of the savings and increased business agility available through the use of virtualization, virtualization management is on the same “old” trajectory as the management of the more established layers of the infrastructure stack.  There are more and more point tools:

• Chasing and collecting more and more data
• Chasing too much knowledge about the intricacies and relationships between the components (In dynamic virtualized  environments these relationships are continuously changing. Chasing an accurate up-to-date picture of the environment at all time is hard and close to impossible.)
• Chasing overly detailed information (Lack of abstraction leads to lack of scalable management solutions)

Virtualization management is relatively a young space, but we already see too many different non-integrated tools. In a recent article, “Managing Performance and Capacity in Virtualized Server and Desktop Environments” (registration required), covering only performance monitoring tools, analyst Bernd Herzog mentions twenty-four different monitoring tools in six different categories: resource and availability management, infrastructure performance management, application performance management, transaction performance management, end user experience management and virtual desktop management. The implication is that to gain visibility and try to assure the application service, one needs to buy six separate point tools. But do you really achieve application service assurance by buying those tools? When a problem occurred somewhere in the environment, which tool would you look at to find out where the problem is?

The trajectory leads to management that is too complex, not scalable, and provides limited value towards the end goal of a self-managed environment.  Instead of reducing the management complexity and the operational costs the current trends only contribute to the increasing management nightmare.

We must stop the trend. “Less is More!” We must look for management solutions that utilize novel approaches to get us on the road to self-managed orchestrated environments. Reversing the current trends of chasing too much information and trying to discover, represent and rely on continuously changing topological relationships. This is the only way we will be able to scale and meet the challenges of the new dynamically changing environments.