Towards smarter live migration: Minimizing SLO violations and costs

Abstract

Data centers employ live virtual machine (VM) migration to optimize resource usage while ensuring continuous execution of guest operating systems. Given the current resource utilization, sophisticated algorithms determine when and where to migrate which VMs. Surprisingly little attention, however, is given to selecting the appropriate migration technique based on specific host and guest workload characteristics. This work first shows that relying on a single live migration algorithm leads to significantly more Service-Level Objective (SLO) violations and higher resource usage than adaptively selecting the most suitable migration algorithm. Building on this observation, we then present an intelligent live migration framework that selects the most appropriate live migration algorithm based on SLOs and operational cost factors, using a multi-objective optimization approach. Through a comprehensive evaluation across diverse hotspot and consolidation scenarios, we demonstrate that the presented framework is able to substantially reduce SLO violation while optimizing key operational metrics. The framework reduces the total migration time by a factor of 1.5 and decreases SLO violations by nearly an order of magnitude compared to the predominantly used pre-copy method. Moreover, it achieves near-optimal VM migration technique selection compared to an Oracle under varying workload conditions. The results indicate that intelligent selection of live migration algorithms can significantly enhance both application performance and resource efficiency in virtualized environments.