Latency-Sensitive Service Function Chains Intelligent Migration in Satellite Communication Driven by Deep Reinforcement Learning

Abstract

Satellite communication technology solves the problem that the traditional wired network infrastructure is difficult to achieve global communication coverage. However, factors such as satellite orbits introduce frequent changes to the network topology, and challenges like satellite failures and communication link interruptions are prevalent. In the face of these issues, service function chain (SFC) migration becomes a crucial method for swiftly adjusting SFCs during faults, maintaining service continuity and availability. This article proposes a latency-sensitive SFC migration algorithm tailored to satellite networks. The algorithm first models the satellite network as a multi-domain virtual network, capturing the constraints faced during SFC migration. Subsequently, a deep reinforcement learning algorithm integrated attention mechanism is designed to more accurately capture and understand the complex network environment and dynamic satellite network topology and derive optimal SFC migration strategies for superior performance. Finally, through experimentation and evaluation of the deep reinforcement learning-driven latency-sensitive service function chain intelligent migration algorithm (LS-SFCM) in satellite communication, this study validates the effectiveness and superior performance of the algorithm in latency-sensitive scenarios. It provides a new avenue for enhancing the service quality and efficiency of satellite communication networks.