Task Scheduling Algorithm Using Covariance Matrix Adaptation Evolution Strategy (CMA-ES) in Cloud Computing

Subject Areas : Cloud, Cluster, Grid and P2P Computing

Ghazaal Emadi ¹ , Amir Masoud Rahmani ² , Hamed Shahhoseini ³

1 - Science and Research Branch, Islamic Azad University, Tehran, Iran.
2 - Department of Computer Engineering Science and Research Branch, Islamic Azad University, Tehran, Iran.
3 - Science and Research Branch, Islamic Azad University, Tehran, Iran.

Received: 2017-03-03 Accepted : 2017-07-12 Published : 2017-08-01

Keywords: Convariance Matrix Adaptation Evolution Strategy(CMA-ES), cloud computing, Task Scheduling, Virtual Machines(Vms),

Abstract :

The cloud computing is considered as a computational model which provides the uses requests with resources upon any demand and needs.The need for planning the scheduling of the user's jobs has emerged as an important challenge in the field of cloud computing. It is mainly due to several reasons, including ever-increasing advancements of information technology and an increase of applications and user needs for these applications with high quality, as well as, the popularity of cloud computing among user and rapidly growth of them during recent years. This research presents the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), an evolutionary algorithm in the field of optimization for tasks scheduling in the cloud computing environment. The findings indicate that presented algorithm, led to a reduction in execution time of all tasks, compared to SPT, LPT, and RLPT algorithms.Keywords: Cloud Computing, Task Scheduling, Virtual Machines (VMs), Covariance Matrix Adaptation Evolution Strategy (CMA-ES)

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