Adaptive Virtual Machine Consolidation Based on Autoformer and Enhanced Double Q-Network for Energy-Efficient Cloud Data Center

As the scale of cloud data centers continues to expand, energy consumption has become a critical issue. Virtual machine (VM) consolidation is a key technology for improving resource utilization and reducing energy consumption, yet it remains challenging to effectively balance energy efficiency with service level agreement violations (SLAV) in dynamic cloud environments. This paper proposes an adaptive VM consolidation strategy based on Autoformer and an enhanced dual Q-Network, referred to as AEDQN-VMC. The approach consists of three integrated components: 1) Autoformer-based load detection, which leverages an autocorrelation mechanism to decompose time-series data into multi-scale trend and periodic components; 2) a VM selection method that integrates the Pearson correlation coefficient and migration time to optimize the selection of VMs for migration; and 3) an enhanced dual Q-Network for VM placement, incorporating the upper confidence bound (UCB) and adaptive learning rate (ALR) to improve the exploration-exploitation trade-off. Extensive experiments on real-world cloud workload traces (PlanetLab, Google Cluster, and Alibaba datasets) demonstrate that the proposed method significantly outperforms state-of-the-art benchmarks such as PABFD, AD-VMC, and AMO-VMC. Specifically, it achieves maximum reductions of 46.5% in energy consumption and 74.2% in SLAV rate. Ablation studies further validate the contribution of each component and confirm the synergistic effect of the overall architecture. The results highlight the potential of AEDQN-VMC as an efficient and reliable solution for sustainable cloud data center operations.