task scheduling algorithm using covariance matrix adaptation evolution strategy (cma-es) in cloud computing

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 everincreasing 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, rlpt, ga and pso algorithms.