Question

How do companies develop a "data center BMI"?

Answer
By Justin Stoltzfus | Last updated: July 10, 2017
Presented by Turbonomic

Modern companies can assess their data center and virtualization operations in many different ways. Many of the most popular models involve analyzing system and application performance, and figuring out how to maximize efficiency with a given set of resources.

One interesting way to assess enterprise networks is to come up with a “data center BMI” to understand how well systems are working, and how close they are to a desired state of performance and resource use.

In this analogy, calories are equated to the supply of infrastructure over storage computing and network segments. Base metabolic rate is the normal application demand, with additional calorie output representing additional application demand due to peak use. The desired state, in this model, represents net daily calories.

In many ways, the analogy can be a useful one – in the sense that companies want to get to a desired state, and many companies can do better in maximizing their use of data center operations. Both the emotional and practical sides of analyzing a BMI also pertain to the analogy: getting the “hard numbers” on performance gets companies closer to action.

To assess the “data center BMI” in this analogy, companies would need to assess performance and resource use through real-time and continuous measurement. Another helpful way to do this is to research the current state of the system, and contrast it to the desired state of optimal resource use.

In addition, there are environmental factors surrounding a data center's BMI. For instance, the fact that many companies operate in a “cloud heterogeneous environment” or multi-cloud system will have a bearing on the assessment and the solution toward a better data center metric.

One fundamental way to achieve a better data center BMI is to use automated systems to help elements of an IT architecture to network more efficiently.

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Data Centers Emerging Technology Big Data Data Science

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Written by Justin Stoltzfus | Contributor, Reviewer

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Justin Stoltzfus is a freelance writer for various Web and print publications. His work has appeared in online magazines including Preservation Online, a project of the National Historic Trust, and many other venues.

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