A survey on PSO based meta-heuristic scheduling mechanism in cloud computing environment

With the increasing of the scale of task or request and dynamic nature of cloud resources, it gives signif-icant issues of load balancing, resource utilization, task allocation, and system performance and so on. To solve those problems many researchers have applied different types of scheduling techniques. But meta -heuristic scheduling is the most accomplish preferred outcomes over conventional heuristics and hybrid scheduling. Among various meta-heuristics algorithms, PSO is a famous metaheuristic technique to solved optimization issue. PSO is appropriate for dynamic task scheduling, workflow scheduling and load balancing. PSO has a strong worldwide searching capability toward the start of the run and a nearby pur-suit close to the furthest limit of the run. Therefore, it has been generally utilized in different applications and has made incredible progress. In this paper a systematically reviews is done on different types of par-ticle swarm optimization (PSO) based scheduling strategy with set of challenges and future direction. (c) 2021 The Authors. Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

This study provides a systematic review of scheduling strategies based on particle swarm optimization (PSO), along with the challenges and future directions.