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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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2021-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | http://dx.doi.org/10.1080/08839514.2021.1987708 |
| Summary: | 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. |
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| ISSN: | 0883-9514 1087-6545 |