Reinforcement Learning for Task Scheduling in Edge Scenarios

The exponential growth of IoT data has created critical challenges for real-time processing, as traditional cloud computing struggles to meet latency-sensitive requirements. This study presents an innovative edge-cloud collaborative scheduling framework featuring: A deep policy gradient-based scheduler employing asynchronous A3C reinforcement learning for distributed decision-making, eliminating single points of failure; A residual recurrent neural network (R2N2) model that leverages nonlinear feature extraction and temporal pattern analysis to accurately predict workload-resource dynamics. The system orchestrates task demands and resource allocation through a five-stage workflow encompassing parameter parsing, intelligent scheduling prediction, constraint validation, targeted task distribution, and online model updating. Experimental results demonstrate the solution's effectiveness in achieving superior responsiveness for time-critical applications while maintaining high QoS standards. The distributed architecture exhibits remarkable environmental adaptability and scalability, providing robust support for high-concurrency IoT scenarios.