Backpressure Matching Learning‐Based Intelligent Service Management for Multimodal Communication Networks

ABSTRACT

The large‐scale deployment of the Internet of Things (IoT) and the advancement of intelligent service management impose stringent delay requirements, as stable low latency and synchronous data arrival are critical. Traditional microservice data processing suffers from prominent challenges: over‐reliance on single‐edge servers, lack of backpressure awareness, and neglect of dynamic edge‐to‐edge offloading, leading to severe load imbalance. To address these issues, this paper proposes a backpressure matching learning‐based intelligent service management algorithm. First, an intelligent service management architecture for multimodal communication networks is constructed. Next, a service data processing optimisation problem is presented to minimise the weighted sum of service processing delay and delay section deviation. The problem is transformed into three sub‐problems: microservice embedding, edge‐to‐edge offloading, and computing resource allocation. Then, a backpressure matching learning‐based optimisation algorithm is introduced to solve the microservice embedding sub‐problem and edge‐to‐edge offloading sub‐problem. On this basis, a convex optimisation method is applied to derive the optimal computing resource allocation strategy. Simulation results show that the proposed method can effectively improve microservice data processing efficiency and boost the overall delay performance.