Computer Science – Distributed – Parallel – and Cluster Computing
Scientific paper
2011-05-12
Computer Science
Distributed, Parallel, and Cluster Computing
10 pages, Submitted to ACM SOCC 2011
Scientific paper
At present there are a number of barriers to creating an energy efficient workload scheduler for a Private Cloud based data center. Firstly, the relationship between different workloads and power consumption must be investigated. Secondly, current hardware-based solutions to providing energy usage statistics are unsuitable in warehouse scale data centers where low cost and scalability are desirable properties. In this paper we discuss the effect of different workloads on server power consumption in a Private Cloud platform. We display a noticeable difference in energy consumption when servers are given tasks that dominate various resources (CPU, Memory, Hard Disk and Network). We then use this insight to develop CloudMonitor, a software utility that is capable of >95% accurate power predictions from monitoring resource consumption of workloads, after a "training phase" in which a dynamic power model is developed.
Smith James W.
Sommerville Ian
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