An effective partition-based framework for virtual machine migration in cloud services

As the scale of data centers continues to expand, optimizing resource utilization becomes increasingly critical. Employing virtual machine (VM) migration technology to maintain hosts within an appropriate workload range holds substantial promise for enhancing platform resource utilization, workload equilibrium, and energy efficiency. This study endeavors to reframe virtual machine migration as a partition problem and introduces an integrated framework that adeptly evaluate workload status and precisely identifies the optimal migration target, thus mitigating the expenses associated with virtual machine migration. Our framework commences by employing workload prediction to evaluate host status for determining the most opportune timing for migration. Subsequently, we leverage Service Level Agreements (SLA) violation as the optimization objective to ascertain the optimal status threshold, thereby facilitating effective workload partition of the host. Finally, the framework employs multi-dimensional host resource balance as a guide to schedule host migration in diverse areas, ensuring robust resource utilization post-migration. Experimental results show that compared with three benchmark VM allocation algorithms, SESA, PPRG, and ThrRs. Our framework achieves a significant \(17\%\) increase in multidimensional resource utilization across various types of data centers, accompanied by a noteworthy \(27\%\) reduction in SLA violation rate with fewer time consumption and energy expenditure during VM migration.