Collaborative edge computing (CEC) has emerged as a promising paradigm, enabling edge nodes to collaborate and execute microservices from end devices. Microservice offloading, a fundamentally important problem, decides when and where microservices are executed upon the arrival of services. However, the dynamic nature of the real-world CEC environment often leads to inefficient microservice offloading strategies, resulting in underutilized resources and network congestion. To address this challenge, we formulate an online joint microservice offloading and bandwidth allocation problem, JMOBA, to minimize the average completion time of services. In this paper, we introduce a novel microservice offloading algorithm, DTDRLMO, which leverages deep reinforcement learning (DRL) and digital twin technology. Specifically, we employ digital twin techniques to predict and adapt to changing edge node loads and network conditions of CEC in real-time. Furthermore, this approach enables the generation of an efficient offloading plan, selecting the most suitable edge node for each microservice. Simulation results on real-world and synthetic datasets demonstrate that DTDRLMO outperforms heuristic and learning-based methods in average service completion time.

翻译：协作边缘计算已成为一种有前景的范式，使边缘节点能够协作执行终端设备中的微服务。微服务卸载作为一项根本重要的问题，决定微服务在服务到达时的执行时间和地点。然而，真实世界中协作边缘计算环境的动态特性常常导致低效的微服务卸载策略，造成资源利用不足和网络拥塞。为应对这一挑战，我们形式化了一个在线联合微服务卸载与带宽分配问题JMOBA，以最小化服务的平均完成时间。本文提出一种新型微服务卸载算法DTDRLMO，该算法利用深度强化学习与数字孪生技术。具体而言，我们采用数字孪生技术实时预测并适应协作边缘计算中边缘节点负载和网络条件的变化。此外，该方法能够生成高效的卸载方案，为每个微服务选择最合适的边缘节点。在真实世界与合成数据集上的仿真结果表明，DTDRLMO在平均服务完成时间上优于启发式方法及基于学习的方法。