A migration strategy based on cluster collaboration predictions for mobile edge computing-enabled smart rail system

As an important part of modern transportation, smart rail system need to handle a large number of delay-sensitive and task-intensive tasks in a high-speed mobile state. However, high-speed mobility challenges the traditional information processing modes a lot, such as service interruptions and information congestion. In order to solve these problems, we proposed a service migration strategy based on intelligent agent group cooperative prediction combined with edge computing service migration technology. The aim is to reduce system latency and overhead and ensure service continuity. Firstly, we constructed a cloud-edge-end collaborative scheduling network architecture model for distributed smart rail system is constructed, integrating mobility management and business orchestration to provide effective support for intelligent decision-making. Then we proposed an intelligent grouping collaborative migration strategy by consolidating resources for similar or identical tasks and employing a group negotiation mechanism, where the migration process is divided into four steps: detection, interaction, coordination and execution. Finally, a deep reinforcement learning algorithm is utilized to train multi-agent models for group collaborative prediction in edge migration strategies. The strategy dynamically adjusts task migration between edge nodes and the cloud based on real-time system status and task requirements to optimize its performance. Experimental results show that the proposed architecture and algorithm can effectively reduce total task delay and system overhead. Instead it can guarantee migration rate for task-intensive requirements, and effectively improve the reliability, effectiveness, and safety of smart rail system services. The present study lays a foundation for the future researches on applications of smart rail system.