  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.
