A reinforcement learning approach for dynamic selection of virtual machines in cloud data centres

In recent years Machine Learning techniques have proven to reduce energy consumption when applied to cloud computing systems. Reinforcement Learning provides a promising solution for the reduction of energy consumption, while maintaining a high quality of service for customers. We present a novel single agent Reinforcement Learning approach for the selection of virtual machines, creating a new energy efficiency practice for data centres. Our dynamic Reinforcement Learning virtual machine selection policy learns to choose the optimal virtual machine to migrate from an over-utilised host. Our experiment results show that a learning agent has the abilities to reduce energy consumption and decrease the number of migrations when compared to a state-of-the-art approach.