Comparison of Meta-Heuristic Optimization Algorithms for Solving Optimized Task Scheduling Problems in Fog Environment

Comparison of Meta-Heuristic Optimization Algorithms for Solving Optimized Task Scheduling Problems in Fog Environment
  IJETT-book-cover           
  
© 2023 by IJETT Journal
Volume-71 Issue-3
Year of Publication : 2023
Author : Ruchika, Rajender Singh Chhillar
DOI : 10.14445/22315381/IJETT-V71I3P218

How to Cite?

Ruchika, Rajender Singh Chhillar, "Comparison of Meta-Heuristic Optimization Algorithms for Solving Optimized Task Scheduling Problems in Fog Environment," International Journal of Engineering Trends and Technology, vol. 71, no. 3, pp. 175-183, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I3P218

Abstract
These days, the most popular kind of algorithm being utilized is called a meta-heuristic algorithm. Because the search space in such an algorithm can only be constrained by the best answer, the resulting searching domain is poor, which in turn leads to a long searching process. The reason for this study is to provide a comparative examination of a metaheuristic optimization approach that may be used to address difficulties with task scheduling. When using the recently created and effective swarm intelligence algorithms, determining the solution for the Optimized task scheduling issue in Fog Environment is a tough challenge. An overwhelming number of challenges need to be tackled, including mixed decision variables; diversified restrictions; inherent mistakes; competing aims; and various locally optimum solutions. The behavior of various meta-heuristic algorithms, such as the Multiverse Optimizer(MVO), Improved Multi-Objective Multi-Verse Optimizer (IMOMVO), Moth-Flame Optimizer(MFO), Atom Search Optimization (ASO), Ecogeography-based Optimization (EBO), Queuing Search Algorithm (QSA), and the equilibrium optimizer, is investigated in this work. In earlier research activities, IMOMVO was developed as a solution to address the shortcomings that were discovered in the original MVO as well as its most recent improved version, MVP. This category of approaches is capable of resolving the issue of the avg positioning by improving equations for updating AP based on the best & second-best solutions currently available. The creators of IMOMVO employed many datasets scenarios with various jobs and virtual machines (Vms) to assess the capabilities of the technique while doing an evaluation. The findings of the IMOMVO approach have been validated with the use of standard evaluation criteria, including Vms processing power, task execution time, and throughput. During the task scheduling process, IMOMVO got better outcomes according to the assessment metrics than other methods that are considered to be state-of-the-art.

Keywords
Fog environment, Optimized task scheduling, Meta heuristic optimization technique, MVO, IMOMVO, MFO, ASO, EBO, QSA.

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