Digital Twin-assisted Reinforcement Learning for Resource-aware Microservice Offloading in Edge Computing

arXiv:2403.08687v1 Announce Type: cross
Abstract: 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 …

abstract arxiv collaborative computing cs.lg cs.ni devices digital digital twin dynamic edge edge computing enabling however microservices nature nodes paradigm reinforcement reinforcement learning services twin type