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Energy-efficient VM selection using deep neural networks for scalable cloud computing
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AI
DOI：10.1007/s10586-026-06354-9.png)
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Abstract
En 中文
Energy-efficient virtual machine (VM) migration could be a crucial challenge towards optimizing resource overusage for energy consumption. This paper discusses an efficient method for VM migration based on Q-learning toward dynamic workload balancing of resources across Physical Machines. The research includes energy-aware decision-making and adaptive threshold incorporation for SLA fulfilment and usage minimization. Experimentation results prove the efficiency of the current approach for state-of-the-art algorithms like Updated SESA, Enhanced Dragonfly, and Optimal Meta-Heuristic Elastic Scheduling (OMES). The implementation reduces power consumption by up to 16.53% when compared with the OMES approach, along with a service level agreement violation improvement of about 50% for 500 individual VMs. These results show the strength and efficiency of the proposed work and also emphasize the application of reinforcement learning for addressing critical challenges in cloud computing. It further provides future directions in energy-efficient resource management.
Keywords:
Deep neural networks
Physical machines
Service level agreements
Virtual machine
Smart elastic scheduling algorithm
Modified best fit decreasing
Journal
C
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5.0K
Citations:
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