Solving Task Scheduling Problem in the Cloud Using a Hybrid Particle Swarm Optimization Approach

Salmi Cheikh and Jessie J. Walker
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Salmi Cheikh: Laboratoire de Modélisation, d'Optimisation et de Système Électroniques (LIMOSE), University of M'Hammed Bougara, Boumerdès, Algeria
Jessie J. Walker: STEM Resources, USA

International Journal of Applied Metaheuristic Computing (IJAMC), 2022, vol. 13, issue 1, 1-25

Abstract: Synergistic confluence of pervasive sensing, computing, and networking is generating heterogeneous data at unprecedented scale and complexity. Cloud computing has emergered in the last two decades as a unique storage and computing resource to support a diverse assortment of applications. Numerous organizations are migrating to the cloud to store and process their information. When the cloud infrastructures and resources are insufficient to satisfy end-users requests, scheduling mechanisms are required. Task scheduling, especially in a distributed and heterogeneous system is an NP-hard problem since various task parameters must be considered for an appropriate scheduling. In this paper we propose a hybrid PSO and extremal optimization-based approach to resolve task scheduling in the cloud. The algorithm optimizes makespan which is an important criterion to schedule a number of tasks on different Virtual Machines. Experiments on synthetic and real-life workloads show the capability of the method to successfully schedule task and outperforms many known methods of the state of the art.

Date: 2022
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