Optimizing the Long-Term Efficiency of Users and Operators in Mobile Edge Computing Using Reinforcement Learning

Abstract

Mobile edge computing (MEC) has emerged as a promising paradigm to enhance computational capabilities at the network edge, enabling low-latency services for users while ensuring efficient resource utilization for operators. One of the key challenges in MEC is optimizing offloading decisions and resource allocation to balance user experience and operator profitability. In this paper, we integrate software-defined networking (SDN) and MEC to enhance system utility and propose an SDN-based MEC network framework. Within this framework, we formulate an optimization problem that jointly maximizes the utility of both users and operators by optimizing the offloading decisions, communication and computation resource allocation ratios. To address this challenge, we model the problem as a Markov decision process (MDP) and propose a reinforcement learning (RL)-based algorithm to optimize long-term system utility in a dynamic network environment. However, since RL-based algorithms struggle with large state spaces, we extend the MDP formulation to a continuous state space and develop a deep reinforcement learning (DRL)-based algorithm to improve performance. The DRL approach leverages neural networks to approximate optimal policies, enabling more effective decision-making in complex environments. Experimental results validate the effectiveness of our proposed methods. While the RL-based algorithm enhances the long-term average utility of both users and operators, the DRL-based algorithm further improves performance, increasing operator and user efficiency by approximately 22.4% and 12.2%, respectively. These results highlight the potential of intelligent learning-based approaches for optimizing MEC networks and provide valuable insights into designing adaptive and efficient MEC architectures.