Using power measurements as a basis for workload placement in heterogeneous multi-cloud environments

Distributed data centers for multi-cloud environments usually do not consist of homogeneous hardware as they are not built at the same time by the same owner. Assigning workloads to the most appropriate processing units is therefore a challenging task. In this paper we show how in the context of heterogeneous data centers power consumption can be used as a metric to drive scheduling. We study the performance and energy efficiency of a set of heterogeneous architectures for multiple micro-benchmarks (stressing CPU, memory and disk) and for a real-world cloud application. We observe from our results that some architectures are more energy efficient for disk-intense workloads, whereas others are better for CPU-intense workloads. This study provides the basis for workload characterization and cross-cloud scheduling under constraints of energy efficiency.