Optimizing VM live migration strategy based on migration time cost modeling

The live migration technology of virtual machine is very helpful for dynamic workload balance, server consolidation and fault tolerance in cloud computing environment. It is important to build live migration strategies which lead to low migration time, thus helping reduce migration cost while achieving migration goal. So we look into the topic of building a model to quantitatively predict live migration time. We thoroughly analyze the key parameters that affect the migration time and construct a live migration time cost model based on KVM. The evaluation of time cost model shows that the average prediction accuracy is above 90% in comparison with measured time. Based on time cost model, we propose 2 optimized live migration strategies for different application scenarios, one for load balance and fault tolerance, the other for server consolidation. The evaluation shows our optimized migration strategies can save 35%~50% of cost compared to the random migration strategy. We believe this should be the first comprehensive study of optimizing live migration strategy with migration time cost as a key factor.