A3C-Based and Dependency-Aware Computation Offloading and Service Caching in Digital Twin Edge Networks

Abstract

The combination of Mobile Edge Computing (MEC) and Digital Twin (DT) is anticipated to enhance the quality of mobile application services in the 6G era. However, current research often overlooks service caching and task dependency, which may deteriorate system performance. Moreover, Edge Servers (ESs) have limited computing resources and caching capacities, which require collaboration to meet user demands. To address these challenges, we propose a DT-empowered MEC architecture that supports Mobile Users (MUs) offloading dependency-aware tasks, while considering service caching and edge collaboration. The objective is to jointly optimize computation offloading and resource allocation to minimize the system’s energy consumption. Hence, this problem can be formulated as a Mixed Integer Non-linear Programming (MINLP) problem and addressed by utilizing the Asynchronous Advantage Actor-Critic (A3C)-based method. Extensive simulation results demonstrate that our approach outperforms other benchmark algorithms under various scenarios, significantly reducing energy consumption.

Keywords

• Mobile edge computing
• service caching
• digital twin
• deep reinforcement learning
• task dependency