Digital Twin-assisted Reinforcement Learning for Resource-aware
  Microservice Offloading in Edge Computing
Xiangchun Chen Jiannong Cao Zhixuan Liang Yuvraj Sahni Mingjin Zhang
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.