Hybridization of meta-heuristic algorithm for load balancing in cloud computing environment
Load balancing of tasks on the cloud environment is an important aspect of distributing resources from a data centre. Due to the dynamic computing through the internet; cloud computing agonizes from overloading of requests. Load balancing has to be carried out in such a manner that all virtual machi...
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| Format: | Article |
| Language: | English |
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Springer
2022-06-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157819309267 |
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| author | U.K. Jena P.K. Das M.R. Kabat |
| author_facet | U.K. Jena P.K. Das M.R. Kabat |
| author_sort | U.K. Jena |
| collection | DOAJ |
| description | Load balancing of tasks on the cloud environment is an important aspect of distributing resources from a data centre. Due to the dynamic computing through the internet; cloud computing agonizes from overloading of requests. Load balancing has to be carried out in such a manner that all virtual machines (VM) should have balanced load to achieve optimal utilization of its capabilities. This paper proposes a novel methodology of dynamic balancing of load among the virtual machines using hybridization of modified Particle swarm optimization (MPSO) and improved Q-learning algorithm named as QMPSO. The hybridization process is carried out to adjust the velocity of the MPSO through the gbest and pbest based on the best action generated through the improved Q-learning. The aim of hybridization is to enhance the performance of the machine by balancing the load among the VMs, maximize the throughput of VMs and maintain the balance between priorities of tasks by optimizing the waiting time of tasks. The robustness of the algorithm has been validated by comparing the results of the QMPSO obtained from the simulation process with the existing load balancing and scheduling algorithm. The comparison of the simulation and real platform result shows our proposed algorithm is outperforming its competitor. |
| format | Article |
| id | doaj-art-66a4e4026cfe4bbcab2ae1708d43042e |
| institution | Kabale University |
| issn | 1319-1578 |
| language | English |
| publishDate | 2022-06-01 |
| publisher | Springer |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj-art-66a4e4026cfe4bbcab2ae1708d43042e2025-08-20T03:48:35ZengSpringerJournal of King Saud University: Computer and Information Sciences1319-15782022-06-013462332234210.1016/j.jksuci.2020.01.012Hybridization of meta-heuristic algorithm for load balancing in cloud computing environmentU.K. Jena0P.K. Das1M.R. Kabat2Dept. of Computer Science and Engineering, VSSUT, Burla, Odisha, IndiaDept. of Information Technology, VSSUT, Burla, Odisha, India; Corresponding author.Dept. of Computer Science and Engineering, VSSUT, Burla, Odisha, IndiaLoad balancing of tasks on the cloud environment is an important aspect of distributing resources from a data centre. Due to the dynamic computing through the internet; cloud computing agonizes from overloading of requests. Load balancing has to be carried out in such a manner that all virtual machines (VM) should have balanced load to achieve optimal utilization of its capabilities. This paper proposes a novel methodology of dynamic balancing of load among the virtual machines using hybridization of modified Particle swarm optimization (MPSO) and improved Q-learning algorithm named as QMPSO. The hybridization process is carried out to adjust the velocity of the MPSO through the gbest and pbest based on the best action generated through the improved Q-learning. The aim of hybridization is to enhance the performance of the machine by balancing the load among the VMs, maximize the throughput of VMs and maintain the balance between priorities of tasks by optimizing the waiting time of tasks. The robustness of the algorithm has been validated by comparing the results of the QMPSO obtained from the simulation process with the existing load balancing and scheduling algorithm. The comparison of the simulation and real platform result shows our proposed algorithm is outperforming its competitor.http://www.sciencedirect.com/science/article/pii/S1319157819309267Load balancingWaiting timeExecution timeQ-learningMPSOCloud computing |
| spellingShingle | U.K. Jena P.K. Das M.R. Kabat Hybridization of meta-heuristic algorithm for load balancing in cloud computing environment Journal of King Saud University: Computer and Information Sciences Load balancing Waiting time Execution time Q-learning MPSO Cloud computing |
| title | Hybridization of meta-heuristic algorithm for load balancing in cloud computing environment |
| title_full | Hybridization of meta-heuristic algorithm for load balancing in cloud computing environment |
| title_fullStr | Hybridization of meta-heuristic algorithm for load balancing in cloud computing environment |
| title_full_unstemmed | Hybridization of meta-heuristic algorithm for load balancing in cloud computing environment |
| title_short | Hybridization of meta-heuristic algorithm for load balancing in cloud computing environment |
| title_sort | hybridization of meta heuristic algorithm for load balancing in cloud computing environment |
| topic | Load balancing Waiting time Execution time Q-learning MPSO Cloud computing |
| url | http://www.sciencedirect.com/science/article/pii/S1319157819309267 |
| work_keys_str_mv | AT ukjena hybridizationofmetaheuristicalgorithmforloadbalancingincloudcomputingenvironment AT pkdas hybridizationofmetaheuristicalgorithmforloadbalancingincloudcomputingenvironment AT mrkabat hybridizationofmetaheuristicalgorithmforloadbalancingincloudcomputingenvironment |