A machine learning approach to live migration modeling

Abstract

Live migration is one of the key technologies to improve data center utilization, power efficiency, and maintenance. Various live migration algorithms have been proposed; each exhibiting distinct characteristics in terms of completion time, amount of data transferred, virtual machine (VM) downtime, and VM performance degradation. To make matters worse, not only the migration algorithm but also the applications running inside the migrated VM affect the different performance metrics. With service-level agreements and operational constraints in place, choosing the optimal live migration technique has so far been an open question. In this work, we propose an adaptive machine learning-based model that is able to predict with high accuracy the key characteristics of live migration in dependence of the migration algorithm and the workload running inside the VM. We discuss the important input parameters for accurately modeling the target metrics, and describe how to profile them with little overhead. Compared to existing work, we are not only able to model all commonly used migration algorithms but also predict important metrics that have not been considered so far such as the performance degradation of the VM. In a comparison with the state-of-the-art, we show that the proposed model outperforms existing work by a factor 2 to 5.