Efficient Task Scheduling in Cloud Systems with Adaptive Discrete Chimp Algorithm

Abstract

Successful task scheduling is one of the priority actions to increase energy efficiency, commercial earnings, and customer satisfaction in cloud computing. On the other hand, since task scheduling processes are NP-hard problems, it is difficult to talk about an absolute solution, especially in scenarios with large task numbers. For this reason, metaheuristic algorithms are frequently used in solving these problems. This study focuses on the metaheuristic-based solution of optimization of makespan, which is one of the important scheduling problems of cloud computing. The adapted Chimp Optimization Algorithm, with enhanced exploration and exploitation phases, is proposed for the first time to solve these problems. The solutions obtained from this adapted algorithm, which can use different mathematical functions, are discussed comparatively. The proposed solutions are also tested in the CloudSim simulator for different scenarios and they prove their performance in the cloud environment.