Energy efficient VM scheduling strategies for HPC workloads in cloud data centers

Abstract

In virtualized environments, virtual machine (VM) scheduling strategies incorporated with energy efficient techniques are needed to reduce the operational cost of the system while delivering high Quality of Service (QoS). It is widely accepted that the cost of the energy consumption in the environment is a dominant part of the owner’s budget. However, when considering energy efficiency, VM scheduling decisions become more constrained, leading in the violation of job deadlines and hence compromising QoS. This paper studies energy efficient VM scheduling strategies in virtualized environments to minimize the queue time and makespan under the fulfillment of SLA requirements (i.e., deadline). Specifically, six energy efficient VM scheduling strategies are investigated incorporated with the dynamic voltage and frequency scaling (DVFS) power management technique. They consist of user-oriented and system-oriented policies. The strategies are extensively simulated and compared with three power management governing methods provided at hypervisor level (i.e., userspec, ondemand, and performance). To conduct simulation experiments, we employ real-world high performance computing (HPC) workloads collected from a production data center. For comparison and evaluation, we analyze the: (a) energy consumption, (b) runtime, (c) queue time, (d) makespan, and (e) slowdown ratio. Lastly, we highlight the strengths and weaknesses of the VM scheduling strategies that can help to choose the most appropriate VM scheduling strategy for a given scenario.