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The energy used by cloud data centers (CDCs) to support large volumes of data storage and computation is dramatically increasing as the scope of cloud services continues to expand. This puts a greater burden on the environment and results in higher expenses for cloud providers. Virtualization migration and consolidation have been widely used in current CDCs to achieve ser-vice consolidation and reduce energy consumption (EC). This study divides the fundamental tasks of virtual machine (VM) migration into three portions: determining migration timing, choosing the VMs to migrate out, and selecting the migration destination hosts. An EC levels-based adaptive dynamic threshold method for determining migration timing was proposed, as well as a correlation and utilization-based strategy for selecting the VMs to migrate out and an improved EC-aware best-fit algorithm for selecting the migration destination hosts. The pro-posed algorithms were evaluated using the CloudSim toolbox, and the real VM workload traces from PlanetLab were used as experimental data. According to the experiments, the proposed algorithms reduce EC, service level agreement violation (SLAV), and the number of VM migrations by an average of 15.49%, 7.85%, and 83.32% in comparison to the related state-of-the-art methods and benchmark algorithms. This suggests that the proposed methods outperform other techniques for VM migration, even when the workload necessitates a significant number of VMs or a greater amount of host resources, and improve the quality of service while optimizing energy consumption. However, the experiments were conducted in a simulation platform, which has some drawbacks, leading to the experimental results varying slightly from the actual environment.
Ma et al. (Sun,) studied this question.