Abstract
Cloud computing is a type of parallel and distributed system consisting of a collection of interconnected and virtual computers. One of the fundamental issues in this environment is related to task scheduling. Cloud task scheduling is an NP-hard optimization problem, and many meta-heuristic algorithms have been proposed to solve it. A good task scheduler should adapt its scheduling strategy to the changing environment and the types of tasks. In this paper, modified ant colony optimization for cloud task scheduling is proposed. The goal of modification is to enhance the performance of the basic ant colony optimization algorithm and optimize the task execution time in view of minimizing the makespan of a given tasks set. Our approach proposes self-adapting criteria for the basic ant colony optimization control parameters. Ant colony optimization algorithm and modified algorithm have been simulated using Cloudsim toolkit package. Experimental results showed that modified ant colony optimization outperformed the ant colony optimization algorithm.