Minimizing Total Busy Time for Energy-Aware Virtual Machine Allocation Problems

TL;DR

This paper proposes a heuristic EM algorithm for energy-efficient VM allocation in cloud data centers, focusing on minimizing total busy time and resource wastage, leading to reduced energy consumption.

Contribution

It introduces a novel EM heuristic-based approach that considers total busy time and resource utilization, outperforming existing power-aware VM allocation algorithms.

Findings

EM algorithms reduce total energy consumption compared to state-of-the-art methods.

Heuristics based on VM scheduling criteria improve resource utilization.

Simulation results validate the effectiveness of the proposed approach.

Abstract

This paper investigates the energy-aware virtual machine (VM) allocation problems in clouds along characteristics: multiple resources, fixed interval time and non-preemption of virtual machines. Many previous works have been 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 placement with multiple resources, fixed interval time and non-preemption. We observed that minimizing the sum of total busy time of all physical machines implies minimizing total energy consumption of physical machines. In addition to, if mapping of a VM onto physical machines have the same total busy time then the best mapping has physical machine's remaining available resource minimizing. Based on these observations, we proposed heuristic-based EM algorithm to solve the energy-aware VM allocation with fixed starting time and duration time. In addition, this work studies some heuristics for sorting the list of virtual machines (e.g., sorting by the earliest starting time, or latest finishing time, or the longest duration time first, etc.) to allocate VM. We evaluate the EM using CloudSim toolkit and jobs log-traces in the Feitelson's Parallel Workloads Archive. Simulation's results show that all of EM-ST, EM-LFT and EM-LDTF algorithms could reduce total energy consumption compared to state-of-the-art of power-aware VM allocation algorithms. (e.g. Power-Aware Best-Fit Decreasing (PABFD) [7])).