ACCELERATE THE CLOUD TASK SCHEDULING WITH ANT COLONY OPTIMIZATION

Abstract

Cloud computing is a type of parallel and distributed system consisting of interconnected physical and virtual
machines. One of the fundamental issues in this environment is related to job scheduling. This work is a nondeterministic polynomial time-hard optimization problem, and many meta-heuristic algorithms have been proposed to solve it. A good task scheduler should adapt its planning strategy to the dynamic environment and the types of assignments. In this paper, the modified ant colony optimization to deal with cloud task scheduling is offered and compared to different algorithms such as First Come First Serve or Round Robin. The main goal of adjustment is to enhance the performance of ant colony optimization algorithm and apply it to minimizing the makespan of a given task set in Clouds. The improved algorithm is going to be deployed in the CloudSim toolkit where Round Robin pre-installed. The experimental results show that the suggested approach outperforms the preset one.