Reinforcement Learning Approach for Optimizing Cloud Resource Utilization With Load Balancing
Cloud computing is a technology that enables the delivery of various computing services over the Internet. The Resource Scheduling (RS) and Load Balancing (LB) mechanisms are essential for the cloud to provide consistent results. The submitted tasks by the users are computed on the cloud platform us...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10305171/ |
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| author | Prathamesh Vijay Lahande Parag Ravikant Kaveri Jatinderkumar R. Saini Ketan Kotecha Sultan Alfarhood |
| author_facet | Prathamesh Vijay Lahande Parag Ravikant Kaveri Jatinderkumar R. Saini Ketan Kotecha Sultan Alfarhood |
| author_sort | Prathamesh Vijay Lahande |
| collection | DOAJ |
| description | Cloud computing is a technology that enables the delivery of various computing services over the Internet. The Resource Scheduling (RS) and Load Balancing (LB) mechanisms are essential for the cloud to provide consistent results. The submitted tasks by the users are computed on the cloud platform using its Virtual Machines (VMs). The cloud ensures an ideal LB mechanism, where no VMs will be overloaded or idle. This research paper focuses on this LB mechanism by experimenting in the WorkflowSim environment and computing tasks using the Sipht task dataset. The RS algorithms First Come First Serve (FCFS), Maximum – Minimum (Max – Min), Minimum Completion Time (MCT), Minimum – Minimum (Min – Min), and Round-Robin (RR) are utilized to balance the computational load of VMs. The experiment was conducted in four phases, where the Sipht task dataset varied in task length in each phase. Each phase included sixteen scenarios, where each scenario differed from another by the number of VMs used. The final results of this experiment convey that the load balanced by the algorithms FCFS, Max – Min, MCT, Min – Min, and RR were 51.98 %, 41.71 %, 51.98 %, 59.43 %, and 52.17 %, respectively, across all four phases. Lastly, the Reinforcement Learning (RL) model is suggested to add an intelligence mechanism to LB and optimize the cloud resource utilization using these RS algorithms to provide the best Quality of Service (QoS). |
| first_indexed | 2024-03-10T14:12:51Z |
| format | Article |
| id | doaj.art-954fc14f71294319bd616172f9e1cad9 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-10T14:12:51Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-954fc14f71294319bd616172f9e1cad92023-11-21T00:01:10ZengIEEEIEEE Access2169-35362023-01-011112756712757710.1109/ACCESS.2023.332955710305171Reinforcement Learning Approach for Optimizing Cloud Resource Utilization With Load BalancingPrathamesh Vijay Lahande0Parag Ravikant Kaveri1Jatinderkumar R. Saini2https://orcid.org/0000-0001-5205-5263Ketan Kotecha3https://orcid.org/0000-0003-2653-3780Sultan Alfarhood4https://orcid.org/0009-0001-1268-9613Symbiosis Institute of Computer Studies and Research, Symbiosis International (Deemed University), Pune, IndiaSymbiosis Institute of Computer Studies and Research, Symbiosis International (Deemed University), Pune, IndiaSymbiosis Institute of Computer Studies and Research, Symbiosis International (Deemed University), Pune, IndiaSymbiosis Centre for Applied Artificial Intelligence, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, IndiaDepartment of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaCloud computing is a technology that enables the delivery of various computing services over the Internet. The Resource Scheduling (RS) and Load Balancing (LB) mechanisms are essential for the cloud to provide consistent results. The submitted tasks by the users are computed on the cloud platform using its Virtual Machines (VMs). The cloud ensures an ideal LB mechanism, where no VMs will be overloaded or idle. This research paper focuses on this LB mechanism by experimenting in the WorkflowSim environment and computing tasks using the Sipht task dataset. The RS algorithms First Come First Serve (FCFS), Maximum – Minimum (Max – Min), Minimum Completion Time (MCT), Minimum – Minimum (Min – Min), and Round-Robin (RR) are utilized to balance the computational load of VMs. The experiment was conducted in four phases, where the Sipht task dataset varied in task length in each phase. Each phase included sixteen scenarios, where each scenario differed from another by the number of VMs used. The final results of this experiment convey that the load balanced by the algorithms FCFS, Max – Min, MCT, Min – Min, and RR were 51.98 %, 41.71 %, 51.98 %, 59.43 %, and 52.17 %, respectively, across all four phases. Lastly, the Reinforcement Learning (RL) model is suggested to add an intelligence mechanism to LB and optimize the cloud resource utilization using these RS algorithms to provide the best Quality of Service (QoS).https://ieeexplore.ieee.org/document/10305171/Cloud computingload balancingperformancereinforcement learningresource scheduling |
| spellingShingle | Prathamesh Vijay Lahande Parag Ravikant Kaveri Jatinderkumar R. Saini Ketan Kotecha Sultan Alfarhood Reinforcement Learning Approach for Optimizing Cloud Resource Utilization With Load Balancing IEEE Access Cloud computing load balancing performance reinforcement learning resource scheduling |
| title | Reinforcement Learning Approach for Optimizing Cloud Resource Utilization With Load Balancing |
| title_full | Reinforcement Learning Approach for Optimizing Cloud Resource Utilization With Load Balancing |
| title_fullStr | Reinforcement Learning Approach for Optimizing Cloud Resource Utilization With Load Balancing |
| title_full_unstemmed | Reinforcement Learning Approach for Optimizing Cloud Resource Utilization With Load Balancing |
| title_short | Reinforcement Learning Approach for Optimizing Cloud Resource Utilization With Load Balancing |
| title_sort | reinforcement learning approach for optimizing cloud resource utilization with load balancing |
| topic | Cloud computing load balancing performance reinforcement learning resource scheduling |
| url | https://ieeexplore.ieee.org/document/10305171/ |
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