Energy-Efficient VM Scheduling in IaaS Clouds

Abstract

This paper investigates the energy-aware virtual machine (VM) scheduling problems in IaaS clouds. Each VM requires multiple resources in fixed time interval and non-preemption. Many previous researches proposed to use a minimum number of physical machines; however, this is not necessarily a good solution to minimize total energy consumption in the VM scheduling with multiple resources, fixed starting time and duration time. We observe that minimizing total energy consumption of physical machines in the scheduling problems is equivalent to minimizing the sum of total busy time of all active physical machines that are homogeneous. Based on these observations, we proposed ETRE algorithm to solve the scheduling problems. The ETRE algorithm’s swapping step swaps an allocating VM with a suitable overlapped VM, which is of the same VM type and is allocated on the same physical machine, to minimize total busy time of all physical machines. The ETRE uses resource utilization during executing time period of a physical machine as the evaluation metric, and will then choose a host that minimizes the metric to allocate a new VM. In addition, this work studies some heuristics for sorting the list of virtual machines (e.g., sorting by the earliest starting time, or the longest duration time first, etc.) to allocate VM. Using log-traces in the Feitelson’s Parallel Workloads Archive, our simulation results show that the ETRE algorithm could reduce total energy consumption average by 48 % compared to power-aware best-fit decreasing (PABFD [6]) and 49 % respectively to vector bin-packing norm-based greedy algorithms (VBP-Norm-L1/L2 [15]).