Learn-as-you-go with Megh: Efficient Live Migration of Virtual Machines
Debabrota Basu, Xiayang Wang, Yang Hong, Haibo Chen and Stephane Bressan
National University of Singapore, Institute of Parallel and Distributed Systems, Shanghai Jiao Tong University, Shanghai Jiao Tong University, Shanghai Jiao Tong University, National University of Singapore

We propose a reinforcement learning algorithm, Megh, for live migration of virtual machines that simultaneously reduces the cost of energy consumption and enhances the performance. Megh learns the uncertain dynamics of workloads asit-goes. Megh uses a dimensionality reduction scheme to project the combinatorially explosive state-action space to a polynomial dimensional space. These schemes enable Megh to be scalable and to work in real-time. We experimentally validate that Megh is more cost-effective and time-efficient than the MadVM and MMT algorithms.