Optimized Cloud Resource Allocation Using Genetic Algorithms for Energy Efficiency and QoS Assurance
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908336777068544 |
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| author | Panggabean, Caroline C, Devaraj Verma Gogoi, Bhagyashree Limbu, Ranju Sarker, Rhythm |
| author_facet | Panggabean, Caroline C, Devaraj Verma Gogoi, Bhagyashree Limbu, Ranju Sarker, Rhythm |
| contents | Cloud computing environments demand dynamic and efficient resource management to ensure optimal performance, reduced energy consumption, and adherence to Service Level Agreements (SLAs). This paper presents a Genetic Algorithm (GA)-based approach for Virtual Machine (VM) placement and consolidation, aiming to minimize power usage while maintaining QoS constraints. The proposed method dynamically adjusts VM allocation based on real-time workload variations, outperforming traditional heuristics such as First Fit Decreasing (FFD) and Best Fit Decreasing (BFD). Experimental results show notable reductions in energy consumption, VM migrations, SLA violation rates, and execution time. A correlation heatmap further illustrates strong relationships among these key performance indicators, confirming the effectiveness of our approach in optimizing cloud resource utilization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_17675 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Optimized Cloud Resource Allocation Using Genetic Algorithms for Energy Efficiency and QoS Assurance Panggabean, Caroline C, Devaraj Verma Gogoi, Bhagyashree Limbu, Ranju Sarker, Rhythm Distributed, Parallel, and Cluster Computing Artificial Intelligence Cloud computing environments demand dynamic and efficient resource management to ensure optimal performance, reduced energy consumption, and adherence to Service Level Agreements (SLAs). This paper presents a Genetic Algorithm (GA)-based approach for Virtual Machine (VM) placement and consolidation, aiming to minimize power usage while maintaining QoS constraints. The proposed method dynamically adjusts VM allocation based on real-time workload variations, outperforming traditional heuristics such as First Fit Decreasing (FFD) and Best Fit Decreasing (BFD). Experimental results show notable reductions in energy consumption, VM migrations, SLA violation rates, and execution time. A correlation heatmap further illustrates strong relationships among these key performance indicators, confirming the effectiveness of our approach in optimizing cloud resource utilization. |
| title | Optimized Cloud Resource Allocation Using Genetic Algorithms for Energy Efficiency and QoS Assurance |
| topic | Distributed, Parallel, and Cluster Computing Artificial Intelligence |
| url | https://arxiv.org/abs/2504.17675 |