Capacity Management and Demand Prediction for Next Generation Data Centers

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Abstract
Advances in server, network, and storage virtualization are enabling the creation of resource pools of servers that permit multiple application workloads to share each server in the pool. This paper proposes and evaluates aspects of a capacity management process for automating the efficient use of such pools when hosting large numbers of services. We use a trace based approach to capacity management that relies on i) a definition for required capacity, ii) the characterization of workload demand patterns, iii) the generation of synthetic workloads that predict future demands based on the patterns, and iv) a workload placement recommendation service. A case study with 6 months of data representing the resource usage of 139 workloads in an enterprise data center demonstrates the effectiveness of the proposed capacity management process. Our results show that when consolidating to 8 processor systems, we predicted future per-server required capacity to within one processor 95% of the time. The approach enabled a 35% reduction in processor usage as compared to today's current best practice for workload placement.

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