Reinforcement Learning Framework for Server Placement and Workload Allocation in Multi-Access Edge Computing
This addresses the challenge of real-time response and cost reduction in 5G/6G IoT networks, but it is incremental as it builds on existing RL methods for combinatorial optimization.
The paper tackles the joint combinatorial optimization problem of minimizing network delay and the number of edge servers in multi-access edge computing by proposing a novel reinforcement learning framework with efficient state and action space modeling.
Cloud computing is a reliable solution to provide distributed computation power. However, real-time response is still challenging regarding the enormous amount of data generated by the IoT devices in 5G and 6G networks. Thus, multi-access edge computing (MEC), which consists of distributing the edge servers in the proximity of end-users to have low latency besides the higher processing power, is increasingly becoming a vital factor for the success of modern applications. This paper addresses the problem of minimizing both, the network delay, which is the main objective of MEC, and the number of edge servers to provide a MEC design with minimum cost. This MEC design consists of edge servers placement and base stations allocation, which makes it a joint combinatorial optimization problem (COP). Recently, reinforcement learning (RL) has shown promising results for COPs. However, modeling real-world problems using RL when the state and action spaces are large still needs investigation. We propose a novel RL framework with an efficient representation and modeling of the state space, action space and the penalty function in the design of the underlying Markov Decision Process (MDP) for solving our problem.