Optimization of Job Scheduling in the Cloud Computing Environment Using the Fuzzy Particle Swarm Optimization Algorithm
Nowadays, along with the constant increase of using cloud environment by companies and organizations, scheduling jobs in this environment in an optimum way is of prime importance. Therefore, different algorithms have been suggested for assigning tasks to resources in cloud environments; however, mos...
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University of Qom
2020-09-01
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| Series: | مدیریت مهندسی و رایانش نرم |
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| Online Access: | https://jemsc.qom.ac.ir/article_1271_ede13aaab1e7ff67858848c5cc505740.pdf |
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| author | Shabnam Gharaeian Khosrow Amirizadeh |
| author_facet | Shabnam Gharaeian Khosrow Amirizadeh |
| author_sort | Shabnam Gharaeian |
| collection | DOAJ |
| description | Nowadays, along with the constant increase of using cloud environment by companies and organizations, scheduling jobs in this environment in an optimum way is of prime importance. Therefore, different algorithms have been suggested for assigning tasks to resources in cloud environments; however, most of which do not consider criteria such as balanced load, and reduction of the task completion time. In this work, using the meta-heuristic algorithm of swarm particles optimization (PSO) and fuzzy logic, task completion time is reduced, and, as a result of which, efficiency of using resources is increased. Generally, in a distributed system like cloud environment, tasks are assigned randomly to resources. Hence, total load on the cloud environment could become imbalanced, which reduces system’s efficiency. In this research, PSO and fuzzy logic is used for job scheduling. In addition, the use of simulated annealing (SA) to improve the initial solutions, which are generated randomly, is suggested. Results show that the suggested optimization method can effectively improve criteria like makespan once compared with results of algorithms without optimization, like Ron-robin, and even in comparison to other optimization algorithms, like genetic algorithm. |
| format | Article |
| id | doaj-art-f26c1f5077014fe0948b584e9250afea |
| institution | Kabale University |
| issn | 2538-6239 2538-2675 |
| language | fas |
| publishDate | 2020-09-01 |
| publisher | University of Qom |
| record_format | Article |
| series | مدیریت مهندسی و رایانش نرم |
| spelling | doaj-art-f26c1f5077014fe0948b584e9250afea2025-01-30T20:17:43ZfasUniversity of Qomمدیریت مهندسی و رایانش نرم2538-62392538-26752020-09-016219921510.22091/jemsc.2018.12711271Optimization of Job Scheduling in the Cloud Computing Environment Using the Fuzzy Particle Swarm Optimization AlgorithmShabnam Gharaeian0Khosrow Amirizadeh1Department of Computer Engineering, Garmsar Branch, Islamic Azad University,Garmsar,IranDepartment of Computer Engineering, Garmsar Branch, Islamic Azad University,Garmsar, IranNowadays, along with the constant increase of using cloud environment by companies and organizations, scheduling jobs in this environment in an optimum way is of prime importance. Therefore, different algorithms have been suggested for assigning tasks to resources in cloud environments; however, most of which do not consider criteria such as balanced load, and reduction of the task completion time. In this work, using the meta-heuristic algorithm of swarm particles optimization (PSO) and fuzzy logic, task completion time is reduced, and, as a result of which, efficiency of using resources is increased. Generally, in a distributed system like cloud environment, tasks are assigned randomly to resources. Hence, total load on the cloud environment could become imbalanced, which reduces system’s efficiency. In this research, PSO and fuzzy logic is used for job scheduling. In addition, the use of simulated annealing (SA) to improve the initial solutions, which are generated randomly, is suggested. Results show that the suggested optimization method can effectively improve criteria like makespan once compared with results of algorithms without optimization, like Ron-robin, and even in comparison to other optimization algorithms, like genetic algorithm.https://jemsc.qom.ac.ir/article_1271_ede13aaab1e7ff67858848c5cc505740.pdfcloud computingjob schedulingparticle swarm optimizationfuzzy logicsimulated annealing |
| spellingShingle | Shabnam Gharaeian Khosrow Amirizadeh Optimization of Job Scheduling in the Cloud Computing Environment Using the Fuzzy Particle Swarm Optimization Algorithm مدیریت مهندسی و رایانش نرم cloud computing job scheduling particle swarm optimization fuzzy logic simulated annealing |
| title | Optimization of Job Scheduling in the Cloud Computing Environment Using the Fuzzy Particle Swarm Optimization Algorithm |
| title_full | Optimization of Job Scheduling in the Cloud Computing Environment Using the Fuzzy Particle Swarm Optimization Algorithm |
| title_fullStr | Optimization of Job Scheduling in the Cloud Computing Environment Using the Fuzzy Particle Swarm Optimization Algorithm |
| title_full_unstemmed | Optimization of Job Scheduling in the Cloud Computing Environment Using the Fuzzy Particle Swarm Optimization Algorithm |
| title_short | Optimization of Job Scheduling in the Cloud Computing Environment Using the Fuzzy Particle Swarm Optimization Algorithm |
| title_sort | optimization of job scheduling in the cloud computing environment using the fuzzy particle swarm optimization algorithm |
| topic | cloud computing job scheduling particle swarm optimization fuzzy logic simulated annealing |
| url | https://jemsc.qom.ac.ir/article_1271_ede13aaab1e7ff67858848c5cc505740.pdf |
| work_keys_str_mv | AT shabnamgharaeian optimizationofjobschedulinginthecloudcomputingenvironmentusingthefuzzyparticleswarmoptimizationalgorithm AT khosrowamirizadeh optimizationofjobschedulinginthecloudcomputingenvironmentusingthefuzzyparticleswarmoptimizationalgorithm |