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Zandvakili, Aboozar, Mansouri, Najme, Javidi, Mohammad. (1400). Signature GOA: A novel comfort zone parameter adjustment using fuzzy signature for task scheduling in cloud environment. , 53(1), 61-95. doi: 10.22059/jac.2021.81539
Aboozar Zandvakili; Najme Mansouri; Mohammad Masoud Javidi. "Signature GOA: A novel comfort zone parameter adjustment using fuzzy signature for task scheduling in cloud environment". , 53, 1, 1400, 61-95. doi: 10.22059/jac.2021.81539
Zandvakili, Aboozar, Mansouri, Najme, Javidi, Mohammad. (1400). 'Signature GOA: A novel comfort zone parameter adjustment using fuzzy signature for task scheduling in cloud environment', , 53(1), pp. 61-95. doi: 10.22059/jac.2021.81539
Zandvakili, Aboozar, Mansouri, Najme, Javidi, Mohammad. Signature GOA: A novel comfort zone parameter adjustment using fuzzy signature for task scheduling in cloud environment. , 1400; 53(1): 61-95. doi: 10.22059/jac.2021.81539

Signature GOA: A novel comfort zone parameter adjustment using fuzzy signature for task scheduling in cloud environment

Journal of Algorithms and Computation
دوره 53، شماره 1، شهریور 2021، صفحه 61-95    اصل مقاله (1.47 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/jac.2021.81539
نویسندگان
Aboozar Zandvakili1؛ Najme Mansouri* 2؛ Mohammad Masoud Javidi1
1Department of Computer Science, Shahid Bahonar University of Kerman, Kerman, Iran
2Department of Computer science, Shahid Bahonar University of Kerman, Kerman, Iran
چکیده
Task scheduling in cloud computing plays an essential role for service provider to enhance its quality of service. Grasshopper Optimization Algorithm (GOA) is an evolutionary computation technique developed by emulating the swarming behavior of grasshoppers while searching for food. GOA is easy to implement but it cannot make full utilization of every iteration, and there is a risk of falling into the local optimal. This paper proposes a suitable approach for adjusting the comfort zone parameter based on the fuzzy signatures called signature GOA (SGOA) to balance exploration and exploitation. Then, we propose task scheduling based on SGOA by considering different objectives such as completion time, delay, and the load balancing on the machines. Finally, different algorithms such as Particle Swarm Optimization (PSO), Simulated Annealing (SA), Tabu Search (TS), and multi-objective genetic algorithm, are used for comparison. The results show that among all algorithms, SGOA can be successful in much less iteration.
کلیدواژه‌ها
Task scheduling؛ Fuzzy signature؛ Multi-objective optimization
آمار
تعداد مشاهده مقاله: 430
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