EVMC: An Energy-Efficient Virtual Machine Consolidation Approach Based on Deep Q-Networks for Cloud Data Centers

As the mainstream computing paradigm, cloud computing breaks the physical rigidity of traditional resource models and provides heterogeneous computing resources, better meeting the diverse needs of users. However, the frequent creation and termination of virtual machines (VMs) tends to induce resource fragmentation, resulting in resource wastage in cloud data centers. Virtual machine consolidation (VMC) technology effectively improves resource utilization by intelligently migrating virtual machines onto fewer physical hosts. However, most existing approaches lack rational host detection mechanisms and efficient migration strategies, often neglecting quality of service (QoS) guarantees while optimizing energy consumption, which can easily lead to Service Level Agreement Violations (SLAVs). To address these challenges, this paper proposes an energy-efficient virtual machine consolidation method (EVMC). First, a co-location coefficient model is constructed to detect the fewest suitable VMs on hosts. Then, leveraging the environment-aware decision-making capability of the DQN agent, dynamic VM migration strategies are implemented. Experimental results demonstrate that EVMC outperforms existing state-of-the-art approaches in terms of energy consumption and SLAV rate, showcasing its effectiveness and potential for practical application.