Efficient resource allocation in cloud environment using SHO-ANN-based hybrid approach
The cloud computing paradigm provides services to users in an on-demand fashion using high-speed Internet. This Internet-based computing paradigm provides resources on a rent basis without any fault. Virtual machine resource allocation is one of the challenging concerns in a cloud computing environm...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co. Ltd.
2024-01-01
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| Series: | Sustainable Operations and Computers |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666412724000114 |
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| author | Sanjeev Sharma Pradeep Singh Rawat |
| author_facet | Sanjeev Sharma Pradeep Singh Rawat |
| author_sort | Sanjeev Sharma |
| collection | DOAJ |
| description | The cloud computing paradigm provides services to users in an on-demand fashion using high-speed Internet. This Internet-based computing paradigm provides resources on a rent basis without any fault. Virtual machine resource allocation is one of the challenging concerns in a cloud computing environment. The existing static, dynamic, and Meta-Heuristic approaches provide the solution to the virtual machine allocation problem. These techniques stuck with the local optimal solution. The slow convergence rate leads to the optimal solution locally and fails to provide the optimal solution Globally. This manuscript proposes a hybrid Spotted Hyena optimizer and artificial neural network, named the SHO-ANN technique, to provide a solution to the virtual machine assignment problem. The presented hybrid technique is evaluated and analyzed using performance metrics “Energy Consumption (Kwh) (8.54%), Host Utilization (24.8%), Average Execution Time(ms) (26.33%), SLA Violations (1.33%), and Number of Migrations (Counts) (19.73%)”. The spotted hyena optimizer is used to provide the vast data set to the ANN model for better accuracy. The hybrid approach provides an optimal solution globally with high convergence. The experimental results exhibit that the SHO-ANN outperforms the IqMc, SHO, and Genetic approaches using real workload scenarios and fabricated scenarios. |
| format | Article |
| id | doaj-art-b397c3f256414fdb9237fabba178d511 |
| institution | OA Journals |
| issn | 2666-4127 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | KeAi Communications Co. Ltd. |
| record_format | Article |
| series | Sustainable Operations and Computers |
| spelling | doaj-art-b397c3f256414fdb9237fabba178d5112025-08-20T02:07:35ZengKeAi Communications Co. Ltd.Sustainable Operations and Computers2666-41272024-01-01514115510.1016/j.susoc.2024.07.001Efficient resource allocation in cloud environment using SHO-ANN-based hybrid approachSanjeev Sharma0Pradeep Singh Rawat1Corresponding author.; School of Computing, DIT University, Dehradun, Uttarakhand, IndiaSchool of Computing, DIT University, Dehradun, Uttarakhand, IndiaThe cloud computing paradigm provides services to users in an on-demand fashion using high-speed Internet. This Internet-based computing paradigm provides resources on a rent basis without any fault. Virtual machine resource allocation is one of the challenging concerns in a cloud computing environment. The existing static, dynamic, and Meta-Heuristic approaches provide the solution to the virtual machine allocation problem. These techniques stuck with the local optimal solution. The slow convergence rate leads to the optimal solution locally and fails to provide the optimal solution Globally. This manuscript proposes a hybrid Spotted Hyena optimizer and artificial neural network, named the SHO-ANN technique, to provide a solution to the virtual machine assignment problem. The presented hybrid technique is evaluated and analyzed using performance metrics “Energy Consumption (Kwh) (8.54%), Host Utilization (24.8%), Average Execution Time(ms) (26.33%), SLA Violations (1.33%), and Number of Migrations (Counts) (19.73%)”. The spotted hyena optimizer is used to provide the vast data set to the ANN model for better accuracy. The hybrid approach provides an optimal solution globally with high convergence. The experimental results exhibit that the SHO-ANN outperforms the IqMc, SHO, and Genetic approaches using real workload scenarios and fabricated scenarios.http://www.sciencedirect.com/science/article/pii/S2666412724000114Cloud computingInternetMeta – heuristicsOptimization SLA (service level agreement)SHO (spotted hyena optimizer)Virtual machine |
| spellingShingle | Sanjeev Sharma Pradeep Singh Rawat Efficient resource allocation in cloud environment using SHO-ANN-based hybrid approach Sustainable Operations and Computers Cloud computing Internet Meta – heuristics Optimization SLA (service level agreement) SHO (spotted hyena optimizer) Virtual machine |
| title | Efficient resource allocation in cloud environment using SHO-ANN-based hybrid approach |
| title_full | Efficient resource allocation in cloud environment using SHO-ANN-based hybrid approach |
| title_fullStr | Efficient resource allocation in cloud environment using SHO-ANN-based hybrid approach |
| title_full_unstemmed | Efficient resource allocation in cloud environment using SHO-ANN-based hybrid approach |
| title_short | Efficient resource allocation in cloud environment using SHO-ANN-based hybrid approach |
| title_sort | efficient resource allocation in cloud environment using sho ann based hybrid approach |
| topic | Cloud computing Internet Meta – heuristics Optimization SLA (service level agreement) SHO (spotted hyena optimizer) Virtual machine |
| url | http://www.sciencedirect.com/science/article/pii/S2666412724000114 |
| work_keys_str_mv | AT sanjeevsharma efficientresourceallocationincloudenvironmentusingshoannbasedhybridapproach AT pradeepsinghrawat efficientresourceallocationincloudenvironmentusingshoannbasedhybridapproach |