Structure-aware reinforcement learning for node-overload protection in mobile edge computing

Mobile Edge Computing (MEC) refers to the concept of placing computational capability at the edge of the network to reduce the latency in handling the client requests. The performance of an edge server is adversely affected when it is overloaded, especially if it crashes due to overload and causes service failures. In this paper, a solution to prevent node from getting overloaded is analyzed by introducing an admission control policy. An adaptive admission control policy based low complexity RL (Reinforcement Learning) SALMUT (Structure-Aware Learning for Multiple Thresholds) is validated using several scenarios mimicking real world deployments. This approach performs as well as to the state-of-the-art deep RL algorithms such as PPO (Proximal Policy Optimization) and A2C (Advantage Actor Critic), but requires an order of magnitude less time to train, and outputs easily interpretable policy.