Energy-Aware VM Initial Placement Strategy Based on BPSO in Cloud Computing

Xiong Fu,Lei Qiao,Junchang Wang,Qing Zhao,Lin Zhang

doi:10.1155/2018/9471356

Xiong Fu, Lei Qiao + Show 3 more

Open Access

https://doi.org/10.1155/2018/9471356

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Abstract

In recent years, high energy consumption has gradually become a prominent problem in a data center. With the advent of cloud computing, computing and storage resources are bringing greater challenges to energy consumption. Virtual machine (VM) initial placement plays an important role in affecting the size of energy consumption. In this paper, we use binary particle swarm optimization (BPSO) algorithm to design a VM placement strategy for low energy consumption measured by proposed energy efficiency fitness, and this strategy needs multiple iterations and updates for VM placement. Finally, the strategy proposed in this paper is compared with other four strategies through simulation experiments. The results show that our strategy for VM placement has better performance in reducing energy consumption than the other four strategies, and it can use less active hosts than others.

Highlights

As a new commercial calculation model, cloud computing is the evolution of distributed computing, parallel computing, and grid computing
We present an energy-aware strategy for Virtual machine (VM) initial placement based on binary particle swarm optimization (BPSO)
(23) return Gbest; Algorithm 1: VM initial placement based on BPSO

Summary

Introduction

As a new commercial calculation model, cloud computing is the evolution of distributed computing, parallel computing, and grid computing. Rapid development of virtualization technology provides a new solution for power consumption in a data center. When cloud computing becomes a main developing direction in the future, due to its own server consolidation, online migration, isolation, high availability, flexible deployment, low administrative overhead, and other advantages, there is more space for the development of virtualization. Because data centers in cloud computing have begun to widely use virtualization technology, exploring a VM placement strategy for low energy consumption in a cloud data center provides a new research direction for improving energy efficiency in a data center. Improvement of PSO algorithm is necessary, and this improvement can be applied to solve optimization problems in the discrete space. Kennedy and Eberhart designed corresponding binary version of PSO (BPSO) in 1997 [21], which is used to solve optimization problems in discrete space

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