Power-aware resource allocation in computer clusters using dynamic threshold voltage scaling and dynamic voltage scaling: comparison and analysis

Abstract

One of the major challenges in the high performance computing (HPC) clusters is intelligent power management to improve energy efficiency. The key contribution of the presented work is the modeling of a Power Aware Job Scheduler (PAJS) for HPC clusters, such that the: (a) threshold voltage is adjusted judiciously to achieve energy efficiency and (b) response time is minimized by scaling the supply voltage. The PAJS considers the symbiotic relationship between power and performance and caters the optimization of the both, simultaneously. The key novelty in our work is utilization of the dynamic threshold-voltage scaling (DTVS) for the reduction of cumulative power utilized by each node in the cluster. Moreover, to enhance the performance of the resource scheduling strategies in this work, independent tasks within a job are scheduled to most suitable computing nodes (CNs). This paper analyzes and compares eight scheduling techniques in terms of energy consumption and makespan. Primarily, the most suitable dynamic voltage scaling (DVS) level adhering to the deadline is identified for each of the CNs by the scheduling heuristics. Afterwards, the DTVS is employed to scale down the static, as well as dynamic power by regulating the supply and bias voltages. Finally, the per node threshold scaling is used attain power saving. Our simulation results affirm that the proposed methodology significantly reduces the energy consumption using the DTVS.