Introspection-Based Memory Pruning for Live VM Migration

Virtual Machine (VM) migration is an appealing technique on nowadays cloud platforms to achieve high availability, load balancing and power saving. Unfortunately, migration of VM involves transferring a large amount of data, thereby imposing high overheads on network traffic, and consequently results in significant application performance degradation. In this paper, we propose an introspection-based memory pruning method for fast and effective live VM migration. Firstly, we classify memory pages into five categories including anonymous, inode, kernel, free and cache pages, according to how they are used by OS. Then, upon migration, we drop the free pages which are insignificant and cache pages which are redundant. In this way, a large amount of unnecessary data are precluded, so that the migration time is reduced as well. Our system can classify memory pages into specific categories precisely using introspection. Besides cache pages, we also eliminate the pages that are ever used but are freed later which is different from most of the works that only eliminate free pages which are marked as zero pages by OS. Experiments show that our work achieves preferable reduction (72% on average ) in terms of the total migration time compared with the original pre-copy algorithm within QEMU/KVM.