Reliability-aware failure recovery for cloud computing based automatic train supervision systems in urban rail transit using deep reinforcement learning

Abstract As urban rail transit construction advances with information technology, modernization, information, and intelligence have become the direction of development. A growing number of cloud platforms are being developed for transit in urban areas. However, the increasing scale of urban rail cloud platforms, coupled with the deployment of urban rail safety applications on the cloud platform, present a huge challenge to cloud reliability.One of the key components of urban rail transit cloud platforms is Automatic Train Supervision (ATS). The failure of the ATS cloud service would result in less punctual trains and decreased traffic efficiency, making it essential to research fault tolerance methods based on cloud computing to improve the reliability of ATS cloud services. This paper proposes a proactive, reliability-aware failure recovery method for ATS cloud services based on reinforcement learning. We formulate the problem of penalty error decision and resource-efficient optimization using the advanced actor-critic (A2C) algorithm. To maintain the freshness of the information, we use Age of Information (AoI) to train the agent, and construct the agent using Long Short-Term Memory (LSTM) to improve its sensitivity to fault events. Simulation results demonstrate that our proposed approach, LSTM-A2C, can effectively identify and correct faults in ATS cloud services, improving service reliability.