Heuristic initialization of PSO task scheduling algorithm in cloud computing

Task scheduling is one of the major issues in cloud computing environment. Efficient task scheduling is substantial to attain cost-effective execution and improve resource utilization. The task scheduling problem is classified to be a nondeterministic polynomial time (NP)-hard problem. This feature attracts researchers to utilize nature inspired metaheuristic algorithms. Initializing searching solutions randomly is one of the key features in such optimization algorithms. However, assisting metaheuristic algorithms with effective initialized solutions can significantly improve its performance. In this paper, an improved initialization of particle swarm optimization (PSO) using heuristic algorithms is proposed. Longest job to fastest processor (LJFP) and minimum completion time (MCT) algorithms are used to initialize the PSO. The performance of the proposed LJFP-PSO and MCT-PSO algorithms are evaluated in minimizing the makespan, total execution time, degree of imbalance, and total energy consumption metrices. Moreover, the performance of the proposed algorithms is compared with recent task scheduling methods. Simulation results revealed the effectiveness and superiority of the proposed LJFP-PSO and MCT-PSO compared to the conventional PSO and comparative algorithms. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of King Saud University.

Automated summary

Task scheduling is a significant issue in cloud computing, and this paper proposes an improved initialization method for particle swarm optimization (PSO) using heuristic algorithms. By initializing PSO with longest job to fastest processor (LJFP) and minimum completion time (MCT) algorithms, the performance can be significantly enhanced, compared to traditional PSO and other methods.