Integrated MOPSO algorithms for task scheduling in cloud computing

Article type: Research Article

Authors: Abdullah, Monir^{a; b; *} | Al-Muta’a, Ebtsam A.^b | Al-Sanabani, Maher^b

Affiliations: [a] College of Computers and Information Technology, University of Bisha, Saudi Arabia | [b] Faculty of Computer Science and Information Systems, Thamar University, Yemen

Correspondence: [*] Corresponding author. Monir Abdullah, Tel.: +966 506795062; E-mail: [email protected].

Abstract: Task Scheduling is one of the most challenging problems in cloud computing. It is an NP-Hard and plays an important role in optimizing the use of available resources. Recently, Multi-Objectives Genetic Algorithm (MOGA) is proposed for cloud tasks scheduling. However, the execution time of the GA is higher than Particle Swarm Optimization (PSO), and the convergence is slower. PSO converges fast because it can be implemented without too many parameters and operators. In this paper, Multi-Objectives PSO (MOPSO) and MOPSO with Importance Strategy (IS) (MOPSO_IS) algorithms are proposed. MOPSO algorithm is integrated with the IS to select the global best leader. Furthermore, incorporating a mutation operator in MOPSO_IS resolved the problem of premature convergence to the local Pareto-optimal front. The performance of the proposed algorithms was compared with MOGA and produced better results. The results of the experiments showed that the proposed MOPSO and MOPSO_IS significantly minimized the total task time and average task time and obtained better distribution for tasks on the available resources in a minimal time.

Keywords: Cloud computing, load balancing, swarm intelligence, multi-objectives optimization.

DOI: 10.3233/JIFS-181005

Journal: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 2, pp. 1823-1836, 2019