Open Access

ARTICLE

Soft Computing Based Metaheuristic Algorithms for Resource Management in Edge Computing Environment

by Nawaf Alhebaishi¹, Abdulrhman M. Alshareef¹, Tawfiq Hasanin¹, Raed Alsini¹, Gyanendra Prasad Joshi², Seongsoo Cho³, Doo Ill Chul^4,*

1 Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
2 Department of Computer Science and Engineering, Sejong University, Seoul, Korea
3 School of Software, Soongsil University, Seoul, 06978, Korea
4 Artificial Intelligence Education, Hankuk University of Foreign Studies, Dongdaemun-gu, Seoul, 02450, Korea

* Corresponding Author: Doo Ill Chul. Email:

Computers, Materials & Continua 2022, 72(3), 5233-5250. https://doi.org/10.32604/cmc.2022.025596

Received 29 November 2021; Accepted 30 December 2021; Issue published 21 April 2022

Abstract

In recent times, internet of things (IoT) applications on the cloud might not be the effective solution for every IoT scenario, particularly for time sensitive applications. A significant alternative to use is edge computing that resolves the problem of requiring high bandwidth by end devices. Edge computing is considered a method of forwarding the processing and communication resources in the cloud towards the edge. One of the considerations of the edge computing environment is resource management that involves resource scheduling, load balancing, task scheduling, and quality of service (QoS) to accomplish improved performance. With this motivation, this paper presents new soft computing based metaheuristic algorithms for resource scheduling (RS) in the edge computing environment. The SCBMA-RS model involves the hybridization of the Group Teaching Optimization Algorithm (GTOA) with rat swarm optimizer (RSO) algorithm for optimal resource allocation. The goal of the SCBMA-RS model is to identify and allocate resources to every incoming user request in such a way, that the client's necessities are satisfied with the minimum number of possible resources and optimal energy consumption. The problem is formulated based on the availability of VMs, task characteristics, and queue dynamics. The integration of GTOA and RSO algorithms assist to improve the allocation of resources among VMs in the data center. For experimental validation, a comprehensive set of simulations were performed using the CloudSim tool. The experimental results showcased the superior performance of the SCBMA-RS model interms of different measures.

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Keywords

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