AoI-aware task scheduling in edge-assisted real-time applications

To address the issue where the resource limitations of wireless devices caused state extraction delays that cannot meet the freshness requirements of real-time applications, considering the limited processing capacity of edge nodes, a scheduling method that jointly considered information freshness and real-time performance was proposed. This method initially characterized the task delay before computation and the information freshness after computation by utilizing the system time of the queue and the age of information, respectively. Simultaneously, reasonable deadlines were assigned to each offloaded task to ensure their validity before entering the computation process. Then, the minimum processing rate constraint method was employed to restrict the processing rate during task scheduling, thereby ensuring the real-time nature of task scheduling. Finally, the objective of optimizing long-term task scheduling decisions was achieved based on Lyapunov optimization techniques. Simulation results demonstrate the good performance of the proposed method in both scheduling timeliness and system information freshness.