Reinforcement Learning-Driven Decision-Making for Live Virtual Machine Migration in Fog Computing

Abstract

Virtualization is an essential mechanism in fog computing that enables elasticity and isolation, which in turn helps achieve resource efficiency. To bring high flexibility in a fog environment, migration of virtual machines from one node to another is required. This can be achieved by live virtual machine migration to reduce downtime and delays. Multiple existing studies have discussed live virtual machine migration in a fog environment. However, these studies have some limitations, such as pre-migrating the virtual machines based on mobility prediction only or based on the load only, which causes an issue of late and early handover. Due to the dynamic nature of fog environments, VM migration decisions require consideration of multiple factors. Hence, there is a need to develop a system that considers multiple factors to decide to migrate a virtual machine or not to solve the issue of early and late handover. This study proposes a novel approach to live virtual machine migration that applies reinforcement learning for decision-making. Experiments show that the proposed approach significantly reduces the latency of time-critical applications. The proposed system, outperforms the existing systems in terms of total average reward. The system  outperformed the mobility-only-based system by 97% when tested with two fog nodes and by 80% when tested with sixteen fog nodes in terms of average reward. Further, the proposed system outperforms the load-based system by 50% and 75% when the environment consists of two fog nodes and sixteen fog nodes, respectively. This proved that considering multiple factors in deciding virtual machine migration in a fog environment can be effectively applied in time-critical applications to reduce latency.