Optimizing Energy Efficiency in Cloud Data Centers: A Reinforcement Learning-Based Virtual Machine Placement Strategy
Cloud computing faces growing challenges in energy consumption due to the increasing demand for services and resource usage in data centers. To address this issue, we propose a novel energy-efficient virtual machine (VM) placement strategy that integrates reinforcement learning (Q-learning), a Firef...
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| Format: | Article |
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MDPI AG
2025-05-01
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| Series: | Network |
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| Online Access: | https://www.mdpi.com/2673-8732/5/2/17 |
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| author | Abdelhadi Amahrouch Youssef Saadi Said El Kafhali |
| author_facet | Abdelhadi Amahrouch Youssef Saadi Said El Kafhali |
| author_sort | Abdelhadi Amahrouch |
| collection | DOAJ |
| description | Cloud computing faces growing challenges in energy consumption due to the increasing demand for services and resource usage in data centers. To address this issue, we propose a novel energy-efficient virtual machine (VM) placement strategy that integrates reinforcement learning (Q-learning), a Firefly optimization algorithm, and a VM sensitivity classification model based on random forest and self-organizing map. The proposed method, RLVMP, classifies VMs as sensitive or insensitive and dynamically allocates resources to minimize energy consumption while ensuring compliance with service level agreements (SLAs). Experimental results using the CloudSim simulator, adapted with data from Microsoft Azure, show that our model significantly reduces energy consumption. Specifically, under the lr_1.2_mmt strategy, our model achieves a 5.4% reduction in energy consumption compared to PABFD, 12.8% compared to PSO, and 12% compared to genetic algorithms. Under the iqr_1.5_mc strategy, the reductions are even more significant: 12.11% compared to PABFD, 15.6% compared to PSO, and 18.67% compared to genetic algorithms. Furthermore, our model reduces the number of live migrations, which helps minimize SLA violations. Overall, the combination of Q-learning and the Firefly algorithm enables adaptive, SLA-compliant VM placement with improved energy efficiency. |
| format | Article |
| id | doaj-art-bc13d3b2405142be8f9a9dcd06a8d8f9 |
| institution | OA Journals |
| issn | 2673-8732 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Network |
| spelling | doaj-art-bc13d3b2405142be8f9a9dcd06a8d8f92025-08-20T02:21:52ZengMDPI AGNetwork2673-87322025-05-01521710.3390/network5020017Optimizing Energy Efficiency in Cloud Data Centers: A Reinforcement Learning-Based Virtual Machine Placement StrategyAbdelhadi Amahrouch0Youssef Saadi1Said El Kafhali2Data Science for Sustainable Earth Laboratory, Faculty of Sciences and Techniques, Sultan Moulay Slimane University, Beni Mellal 23010, MoroccoData Science for Sustainable Earth Laboratory, Faculty of Sciences and Techniques, Sultan Moulay Slimane University, Beni Mellal 23010, MoroccoComputer, Networks, Modeling, and Mobility Laboratory (IR2M), Faculty of Sciences and Techniques, Hassan First University of Settat, Settat 26000, MoroccoCloud computing faces growing challenges in energy consumption due to the increasing demand for services and resource usage in data centers. To address this issue, we propose a novel energy-efficient virtual machine (VM) placement strategy that integrates reinforcement learning (Q-learning), a Firefly optimization algorithm, and a VM sensitivity classification model based on random forest and self-organizing map. The proposed method, RLVMP, classifies VMs as sensitive or insensitive and dynamically allocates resources to minimize energy consumption while ensuring compliance with service level agreements (SLAs). Experimental results using the CloudSim simulator, adapted with data from Microsoft Azure, show that our model significantly reduces energy consumption. Specifically, under the lr_1.2_mmt strategy, our model achieves a 5.4% reduction in energy consumption compared to PABFD, 12.8% compared to PSO, and 12% compared to genetic algorithms. Under the iqr_1.5_mc strategy, the reductions are even more significant: 12.11% compared to PABFD, 15.6% compared to PSO, and 18.67% compared to genetic algorithms. Furthermore, our model reduces the number of live migrations, which helps minimize SLA violations. Overall, the combination of Q-learning and the Firefly algorithm enables adaptive, SLA-compliant VM placement with improved energy efficiency.https://www.mdpi.com/2673-8732/5/2/17cloud computingserver consolidationenergy consumptionfirefly optimizationmachine learningQ-learning |
| spellingShingle | Abdelhadi Amahrouch Youssef Saadi Said El Kafhali Optimizing Energy Efficiency in Cloud Data Centers: A Reinforcement Learning-Based Virtual Machine Placement Strategy Network cloud computing server consolidation energy consumption firefly optimization machine learning Q-learning |
| title | Optimizing Energy Efficiency in Cloud Data Centers: A Reinforcement Learning-Based Virtual Machine Placement Strategy |
| title_full | Optimizing Energy Efficiency in Cloud Data Centers: A Reinforcement Learning-Based Virtual Machine Placement Strategy |
| title_fullStr | Optimizing Energy Efficiency in Cloud Data Centers: A Reinforcement Learning-Based Virtual Machine Placement Strategy |
| title_full_unstemmed | Optimizing Energy Efficiency in Cloud Data Centers: A Reinforcement Learning-Based Virtual Machine Placement Strategy |
| title_short | Optimizing Energy Efficiency in Cloud Data Centers: A Reinforcement Learning-Based Virtual Machine Placement Strategy |
| title_sort | optimizing energy efficiency in cloud data centers a reinforcement learning based virtual machine placement strategy |
| topic | cloud computing server consolidation energy consumption firefly optimization machine learning Q-learning |
| url | https://www.mdpi.com/2673-8732/5/2/17 |
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