Performance Degradation-Aware Virtual Machine Live Migration in Virtualized Servers

Abstract

Live migration of virtual machines(VMs) is widely used for system management in virtualized servers. When the loads increase and SLAs of some applications are violated, dynamic migration of virtual machines across physical machines (PMs) has the potential to ensure a high level of meeting the SLAs. Because of consuming extra CPU and bandwidth, application performance may be degraded during the migration process. However, different applications have different performance degradation. We design and implement a VM migration selection method that decides which VMs should be migrated. It can not only eliminate resouce competition on the PM, but also have less performance degradation during the migration process. We propose a performance degration-aware model to analyze applications' performance degradation which is directly sensitive to users. We analyze migration source code and find that memory size, dirty rate and frequent dirty rate are key factors that affect iteration time and downtime. We implement a tool that measures dirty rate and frequent dirty rate before VMs are migrated. we make a distinction between memory iteration phrase and stop-and-copy phrase owing to different performance degradation. The experimental results show that our method is effective.