Highlighted
SteveFM
3 Zinc

Big Data: Deciphering the Data Center

Traditional forecasting and budgeting techniques can make it nearly impossible to correctly determine where to invest limited resources.

As IT and data center management transition from legacy IT models to newer, more dynamic models, they need to monitor usage metrics. It is critical that metrics be measured and analyzed to understand how resources are consumed, why they are consumed, and when demand will rise and fall. By understanding how specific resources are being consumed, data center management can provide an improved and more reliable service. Analyzing and summarizing vast amounts of raw data can show trends in consumption. Mapping these consumption trends to long-term business plans enables more accurate predictions of future data center resource demands based on similar historical records.

To correctly forecast and predict data center resource demand, IT professionals must harness the power of Big Data Analytics to intelligently design their services. While the specific applications of Big Data analytics will vary a great deal based on a particular business, the general concepts are all similar. Analyzing historical records can help identify resource utilization and the magnitude of resource demand over time; this will predict the right resources are available at the right times. The collected metrics coupled with the ability to analyze and plan around them enables elasticity, continuous delivery, and an improved customer experience.

In this Knowledge Sharing article, Andrew Bartley and Rich Elkins explain how using predictive analytics to improve utilization of existing data centers will be particularly useful to any IT professional that has experienced shrinking budgets and increased demands. Andrew and Rich provide generalized instructions along with specific examples of how to analyze historical resource demands and predict future demand that will enable IT professionals to improve utilization of existing resources.

Read the full article.

Labels (1)
0 Kudos