A Bioinspired Method for Optimal Task Scheduling in Fog-Cloud Environment

作     者：Ferzat Anka Ghanshyam G.Tejani Sunil Kumar Sharma Mohammed Baljon 

作者机构：Data Science Application and Research Center(VEBIM)Fatih Sultan Mehmet Vakif UniversityIstanbul34445Turkiye Department of Industrial Engineering and ManagementYuan Ze UniversityTaoyuan320315Taiwan Applied Science Research CenterApplied Science Private UniversityAmman11937Jordan Department of Information SystemCollege of Computer and Information SciencesMajmaah UniversityMajmaah11952Saudi Arabia Department of Computer EngineeringCollege of Computer and Information SciencesMajmaah UniversityMajmaah11952Saudi Arabia 

出 版 物：《Computer Modeling in Engineering & Sciences》 (工程与科学中的计算机建模(英文))

年 卷 期：2025年第142卷第3期

页      面：2691-2724页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 081201[工学-计算机系统结构] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：the Deanship of Postgraduate Studies and Scientific Research at Majmaah University for funding this research work through the project number(R-2025-1567) 

主　　题：Improved ARO fog computing task scheduling GoCJ_Dataset chaotic map levy flight 

摘      要：Due to the intense data flow in expanding Internet of Things(IoT)applications,a heavy processing cost and workload on the fog-cloud side become *** of the most critical challenges is optimal task *** this is an NP-hard problem type,a metaheuristic approach can be a good *** study introduces a novel enhancement to the Artificial Rabbits Optimization(ARO)algorithm by integrating Chaotic maps and Levy flight strategies(CLARO).This dual approach addresses the limitations of standard ARO in terms of population diversity and convergence *** is designed for task scheduling in fog-cloud environments,optimizing energy consumption,makespan,and execution time simultaneously three critical parameters often treated individually in prior *** conventional single-objective methods,the proposed approach incorporates a multi-objective fitness function that dynamically adjusts the weight of each parameter,resulting in better resource allocation and load *** analysis,a real-world dataset,the Open-source Google Cloud Jobs Dataset(GoCJ_Dataset),is used for performance measurement,and analyses are performed on three considered *** are applied with well-known algorithms:GWO,SCSO,PSO,WOA,and ARO to indicate the reliability of the proposed *** this regard,performance evaluation is performed by assigning these tasks to Virtual Machines(VMs)in the resource *** are performed on 90 base cases and 30 scenarios for each evaluation *** results indicated that the proposed algorithm achieved the best makespan performance in 80% of cases,ranked first in execution time in 61%of cases,and performed best in the final parameter in 69% of *** addition,according to the obtained results based on the defined fitness function,the proposed method(CLARO)is 2.52%better than ARO,3.95%better than SCSO,5.06%better than GWO,8.15%better than PSO,and 9.41%better than WOA.