A procedure for machine learning-based live migration modeling

Live migration is one of the core technologies to increase the efficiency of data centers by enabling better power savings, a higher utilization, load balancing, and simplifying maintenance. With service-level agreements (SLA) in place, the overhead of live migration in terms of resources consumed on the host plus the performance reduction and downtime of the migrated VM poses a major obstacle to effectively apply live migration. With various live migration algorithms available, an important question is then which of the algorithms can provide optimal performance while respecting the SLAs. In this work, we propose a versatile model that is able to accurately predict the key metrics of live migration. The machine-learned model is trained with data from over ۱۰,۰۰۰ VM migrations and evaluated for the five live migration algorithms available in the latest QEMU/KVM virtualization environment. The evaluation shows that the proposed model is able to predict the total migration time and the total transferred data with over ۹۰% accuracy, and ۹۰th percentile error of the downtime is ۲۸۰ms.