Energy-Aware Application-Centric VM Allocation for HPC Workloads

Abstract

Virtualized data centers and clouds are being increasingly considered for traditional High-Performance Computing (HPC) workloads that have typically targeted Grids and conventional HPC platforms. However, maximizing energy efficiency, cost-effectiveness, and utilization of data center resources while ensuring performance and other Quality of Service (QoS) guarantees for HPC applications requires careful consideration of important and extremely challenging tradeoffs. An innovative application-centric energy-aware strategy for Virtual Machine (VM) allocation is presented. The proposed strategy ensures high resource utilization and energy efficiency through VM consolidation while satisfying application QoS. While existing VM allocation solutions are aimed at satisfying only the resource utilization requirements of applications along only one dimension (CPU utilization), the proposed approach is more generic as it employs knowledge obtained through application profiling along multiple dimensions. The results of our evaluation show that the proposed VM allocation strategy enables significant reduction either in energy consumption or in execution time, depending on the optimization goals.