Enhancing network function parallelism in mobile edge computing using Deep Reinforcement Learning

Abstract

This paper introduces a Deep Reinforcement Learning (DRL)-based framework to enhance Network Function Parallelism (NFP) in Mobile Edge Computing (MEC). Leveraging Network Function Virtualization (NFV), the proposed framework optimizes service delay by solving a fairness-aware throughput maximization problem for service function chain placement. It aims to maximize the long-term cumulative reward while satisfying Quality of Service (QoS) requirements. The framework also preserves resources for future requests by efficiently managing the initialized network functions distribution. Simulation results demonstrate the superior performance of the proposed framework across various metrics. Specifically, our framework improves the average delay and deployment rate by 1.2% and 2.4% compared to the existing best method.