Memory/Disk Operation Aware Lightweight VM Live Migration Across Data-centers with Low Performance Impact

Abstract

Live virtual machine migration technique allows migrating an entire OS with running applications from one physical host to another, while keeping all services available without interruption. It provides a flexible and powerful way to balance system load, save power and tolerant faults in data centers. Meanwhile, with the stringent requirements of latency, scalability, and availability, an increasing number of applications are deployed across distributed cloud data-centers. However, existing live migration approaches still suffer from long downtime and serious performance degradation in cross data-center scenes due to the mass of dirty retransmission, which limits the ability of cross data-center scheduling. In this paper, we propose a system named Memory/disk operation aware Lightweight VM Live Migration across data-centers with low performance impact (MLLM). It significantly improves the cross data-center migration performance by reducing the amount of dirty data in the migration process. In MLLM, we predict disk read workingset (i.e., more frequently read contents) and memory write workingset (i.e., more frequently write contents) based on the access sequence trace. And then we adjust the migration models and data transfer sequence based on the workingset information. We also present two optimizing methods to filter unused blocks and to de-duplicate data content by a hot data cache, thereby greatly decreasing the amount of data to be transferred. We implement MLLM on the QEMU/KVM platform and conduct several real-world experiments. The experimental results show that our method averagely reduces 67.0% of total migration time and 41.6% service downtime over existing methods.