Virtual Machine-Based Task Scheduling Algorithm in a Cloud Computing Environment

Zhifeng Zhong

doi:10.1109/TST.2016.7787008

Virtual Machine-Based Task Scheduling Algorithm in a Cloud Computing Environment

2016-12-01

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DOI：10.1109/TST.2016.7787008

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Virtualization technology has been widely used to virtualize single server into multiple servers, which not only creates an operating environment for a virtual machine-based cloud computing platform but also potentially improves its efficiency. Currently, most task scheduling-based algorithms used in cloud computing environments are slow to convergence or easily fall into a local optimum. This paper introduces a Greedy Particle Swarm Optimization (G&PSO) based algorithm to solve the task scheduling problem. It uses a greedy algorithm to quickly solve the initial particle value of a particle swarm optimization algorithm derived from a virtual machine-based cloud platform. The archived experimental results show that the algorithm exhibits better performance such as a faster convergence rate, stronger local and global search capabilities, and a more balanced workload on each virtual machine. Therefore, the G&PSO algorithm demonstrates improved virtual machine efficiency and resource utilization compared with the traditional particle swarm optimization algorithm.

Keyword:

cloud computing

virtual machine

G&PSO algorithm

Virtual Machine-Based Task Scheduling Algorithm in a Cloud Computing Environment

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