Industrial 5G Service Allocation Based on A Novel Bidirectional Selection Approach

This paper proposes a server-client mutual selection strategy based on multiple resource indicators, providing a balanced selection algorithm for edge compu-ting services for industrial Automated Guided Vehicles (AGVs). Firstly, based on the service popularity and Zipf's law, the type of service primarily carried by each Remote Unit (RU) is determined, and RUs are divided into work area slices based on the similarity of service popularity. Nest, the AGV side first sends a forward service request to the RU side based on the requirements and urgency of work tasks, and then the RU side reversely selects the AGV from the perspective of optimal resource allocation. This paper comprehensively considers multiple factors such as matching of process service content, user de-lay limit, and computing and communication resources, and can solve the problem of dynamically allocating resources in the edge-cloud computing network in a balanced matching manner in scenarios like intelligent factories and ports. Simultaneously, this paper adopts the strategy of bidirectional selection, achieving a trade-off between user experience and economic benefits for users and operators.