期刊
IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING
卷 8, 期 4, 页码 1881-1897出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCCN.2022.3195503
关键词
Communication systems; mobile communications; Markov process; adaptive control; traffic control (communications)
资金
- MITACS Accelerate [IT16364]
Mobile Edge Computing (MEC) is important because it provides benefits such as reduced latency, reduced network congestion, and improved performance of applications. This research presents an adaptive admission control policy based on reinforcement learning algorithm to prevent edge node from getting overloaded, and validates the proposed solution through simulated experiments.
Mobile Edge Computing (MEC) involves placing computational capability and applications at the edge of the network, providing benefits such as reduced latency, reduced network congestion, and improved performance of applications. The performance and reliability of MEC degrades significantly when the edge server(s) in the cluster are overloaded. In this work, an adaptive admission control policy to prevent edge node from getting overloaded is presented. This approach is based on a recently-proposed low complexity RL (Reinforcement Learning) algorithm called SALMUT (Structure-Aware Learning for Multiple Thresholds), which exploits the structure of the optimal admission control policy in multi-class queues for an average-cost setting. We extend the framework to work for node overload-protection problem in a discounted-cost setting. The proposed solution is validated using several scenarios mimicking real-world deployments in two different settings - computer simulations and a docker testbed. Our empirical evaluations show that the total discounted cost incurred by SALMUT is similar to 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, outputs easily interpretable policy, and can be deployed in an online manner.
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