An Efficient Online Computation Offloading Approach for Large-Scale Mobile Edge Computing via Deep Reinforcement Learning

Mobile edge computing (MEC) has been envisioned as a promising paradigm that could effectively enhance the computational capacity of wireless user devices (WUDs) and quality of experience of mobile applications. One of the most crucial issues of MEC is computation offloading, which decides how to offload WUDs’ tasks to edge severs for further intensive computation. Conventional mathematical programming-based offloading approaches could face troubles in dynamic MEC environments due to the time-varying channel conditions (caused primarily by WUD mobility). To address the problem, reinforcement learning (RL) based offloading approaches have been proposed, which develop offloading policies by mapping MEC states to offloading actions. However, these approaches could fail to converge in large-scale MEC due to the exponentially-growing state and action spaces. In this article, we propose a novel online computation offloading approach that could effectively reduce task latency and energy consumption in dynamic MEC with large-scale WUDs. First, a RL-based computation offloading and energy transmission algorithm is proposed to accelerate the learning process. Then, a joint optimization method is adopted to develop the allocating algorithm, which obtains near-optimal solutions for energy and computation resources allocation. Simulation results show that the proposed approach can converge efficiently and achieve significant performance improvements over baseline approaches.

1 Introduction

With the rapid development of wireless communication technology and wide popularity of smart mobile devices, recent years have witnessed an explosive growth of mobile applications [1], such as face recognition payment, online mobile gaming, virtual/augmented reality (VR/AR), etc. Most of these mobile applications are computation-intensive and delay-critical [2], [3], while the mobile devices (e.g., smartphones, wearable devices) are typically equipped with limited computational capacity and battery energy [4]. The tension between computation-intensive applications and resource-constrained devices poses a great challenge to improve the quality of experience (QoE) for mobile users [5], [6].