Live migration is an essential feature in virtual infrastructure and cloud computing datacenters. Using live migration, virtual machines can be online migrated from a physical machine to another with negligible service interruption. Load balance, power saving, dynamic resource allocation, and high availability algorithms in virtual data-centers and cloud computing environments are dependent on live migration. Live migration process has six phases that result in live migration overhead. Currently, virtual datacenters admins run live migrations without an idea about the migration cost prediction and without recommendations about the optimal timing for initiating a VM live migration especially for large memory VMs or for concurrently multiple VMs migration. Without cost prediction and timing optimization, live migration might face longer duration, network bottlenecks and migration failure in some cases. The previously proposed timing optimization approach is based on using machine learning for live migration cost prediction and the network utilization predict ion of the cluster. In this paper, we show how to integrate our machine learning based timing optimization algorithm with VMware vSphere. This integration deployment proves the practicality of the proposed algorithm by presenting the building blocks of the tools and backend scripts that should run to implement this timing optimization feature. The paper shows also how the IT admins can make use of this novel cost prediction and timing optimization option as an integrated plug-in within VMware vSphere UI to be notified with the optimal timing recommendation in case of a having live migration request.
Live Migration Timing Optimization Integration with VMware Environments