An Enhanced Firefly Algorithm for Time ‎‎Shared Grid Task ‎Scheduling

Grid computing is a computational paradigm that emerged to ‎‎handle the increasing demand for ‎computational resources. Several metaheuristics methods ‎‎have been applied ‎to tackle the grid task scheduling problem. ‎‎These metaheuristics generally generate good but not optimal ‎‎task ‎schedules. Th...

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Veröffentlicht in:Applied artificial intelligence Jg. 35; H. 15; S. 1567 - 1586
1. Verfasser: Yousif, Adil
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Philadelphia Taylor & Francis 15.12.2021
Taylor & Francis Ltd
Taylor & Francis Group
Schlagworte:
Algorithms
Computational grids
Genetic algorithms
Heuristic methods
Permutations
Scheduling
Tabu search
Task scheduling
ISSN:0883-9514, 1087-6545
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Zusammenfassung:Grid computing is a computational paradigm that emerged to ‎‎handle the increasing demand for ‎computational resources. Several metaheuristics methods ‎‎have been applied ‎to tackle the grid task scheduling problem. ‎‎These metaheuristics generally generate good but not optimal ‎‎task ‎schedules. The aim of this paper is to design and ‎‎implement a grid task scheduling mechanism to map clients' tasks to ‎‎ ‎available resources in order to finish the submitted tasks ‎‎within the optimal execution time. The paper proposes ‎an ‎‎enhanced time shared metaheuristics mechanism based on ‎‎Firefly Algorithm to ‎‎improve the grid job scheduling process. The proposed mechanism utilizes the Smallest Position ‎Value (SPV) technique to handle the scheduling problem as ‎permutations. Experiments using ‎‎simulations and real workload traces were ‎conducted to study ‎‎the performance of the proposed enhanced time shared ‎‎metaheuristic scheduling mechanism. ‎Empirical results revealed ‎‎that the proposed timed shared ‎metaheuristic algorithm can efficiently reduce the makespan time to 1851 compared with 3482, 3185 for Tabu search and genetic algorithm, respectively.
Bibliographie:ObjectType-Article-1
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ISSN:0883-9514
1087-6545
DOI:10.1080/08839514.2021.1987708