A Comparative Study of Metaheuristics based Task Scheduling in Distributed Environment

Abstract

Objectives: To make an extensive survey on various meta-heuristic and hybrid task scheduling along with their classification patterns and to find the scope of improvement in these techniques. Method: This paper carries to the deep study of 99 reputed research papers from Springer, IEEE, Elsevier, Scopus indexed; SCI indexed of well-known renowned journals. These research papers are selected by taking into consideration of relevance to research area. These scheduling algorithms are compared in terms of their performance metrics, environments and results. Findings: This paper described that there are various renowned researchers who have proposed various meta-heuristic task scheduling techniques to achieve the optimum results but after the extensive survey of various scheduling techniques based on genetic, Simulated Annealing (SA), ACO, PSO and hybrid reveals that a lot of dimensions are yet to be explored in terms of datacenter cost, virtual machine migration, energy consumption and Service-Level Agreement etc. Application: It discusses numerous meta-heuristic based task scheduling algorithms with their classification patterns so as to find the gap in the already proposed algorithm and suggest the untouched areas for the further research.