Heuristics for Energy-Aware VM Allocation in HPC Clouds

Abstract

AbstractHigh performance computing (HPC) clouds have become more popular for users to run their HPC applications on cloud infrastructures. Reduction in energy consumption (kWh) for these cloud systems is of high priority for any cloud provider. In this paper, we first study the energy-aware allocation of virtual machines (VMs) in HPC cloud systems along two dimensions: multi-dimensional resources and interval times of virtual machines. On the one hand, we present an example showing that using bin-packing heuristics (e.g. Best-Fit Decreasing) to minimize the number of physical servers could not lead to a minimum of total energy consumption. On the other hand, we find out that minimizing total energy consumption is equivalent to minimizing the sum of total completion time of all physical machines. Based on this finding, we propose the MinDFT-ST and MinDFT-FT algorithms to place the VMs onto the physical servers in such a way that minimizes the total completion times of all physical servers. Our simulation results show that MinDFT-ST and MinDFT-FT could reduce the total energy consumption by 22.4% and respectively 16.0% compared with state-of-the-art power-aware heuristics (such as power-aware best-fit decreasing) and vector bin-packing norm-based greedy algorithms (such as VBP-Norm-L1, VBP-Norm-L2, VBP-Norm-L30).KeywordsVirtual MachineTotal Energy ConsumptionHigh Performance ComputingCloud ProviderPhysical ServerThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.