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Introduction
Big Data is all the rage these days. Organizations are harnessing the power of data to gain insights and improve decision-making. It’s hard to read an article that doesn’t contain the phrase and it’s often difficult for CIOs to determine just how “Big Data” can help them in their day-to-day work.
In what might seem like a game of buzzword bingo, a new cloud-based service actually leverages aggregated data to help CIOs better manage their VMware environments. On August 20, 2012, the Wikibon community discussed how this new player—CloudPhysics—is working to bring big data to bear to make improved virtual operations a reality.
Workload planning and proofs of concept
For CIOs, running proofs-of-concept to determine workload needs has been a constant task. Under the old paradigm of single server to single app, such sizing calculations were relatively simple to perform, although they still took staff time, and an error in the calculations would result in poor implementation. But the simplicity was there and pretty scalable. As needs increased, CIOs just bought more hardware to throw at the problem. It was an efficient calculation made possible by the one-to-one nature of server to application.
Today’s IT environment barely resembles the environment of just ten years ago. Whereas the older environment simply added server after server after server to meet new needs, CIOs today are leveraging extremely complex virtual environments with shared servers, shared networks, and shared storage. These are much more complex, and change is constant, with applications being dynamically migrated among hosts as administrators intervene or as automated rules governing workload management indicate that workloads are better suited elsewhere.
Further, as IT organizations begin embracing cloud providers as extensions of the primary data center infrastructure, these environments will continue to grow in complexity.
Due to the intertwined web that is today’s data center environment, creating proofs-of-concept environments intended to gauge how new applications may interact and integrate with the production environment is very difficult and an increasingly time-consuming task. Worse, it’s next to impossible to determine just how a new application will really work under a truly dynamic environment that changes every minute.
A solution
For every problem, there is a solution. Someone, somewhere has probably devised a solution for most of the application integration problems. That’s where CloudPhysics comes in. This startup leverages crowdsourcing to create data sets that will allow clients to determine the kind of information they need when they deploy a new application. In its model, people will share their application performance information with CloudPhysics anonymously, and this data will be aggregated for consumption by others.
Suddenly, a CIO shifts from “best guess” decisions around sizing for application needs to being able to make data-driven decisions based on real-world information by leveraging a community.
A significant impact
The potential impact is significant. CIOs get a better result at the end, and they no longer need to task staff with months-long costly proofs of concept that divert staff from business-facing work. New solutions can be deployed much faster without the need to perform proof of concept performance testing meaning that IT can get solutions to market much more quickly than otherwise possible.
Action Item: Although words like "cloud", "Big Data" and "crowdsourcing" are often vague and overused, CloudPhysics provides a prime example for when these forces come together in a tangible benefit for CIOs. Those who have significant application performance testing needs should look at CloudPhysics and consider participating in the company's ongoing application performance gathering and assessment ventures. By doing so, they may be able to leverage this cloud-based service and eliminate what is a significant internal task, resulting in a better overall outcome and more IT staff time to be devoted to solutions development.
Footnotes: