Safety-Aware Scheduling of Real-Time Control Systems With Burst Computing Tasks

Ting Cheng, Yonghui Liang, Hui Li, Qimin Xu, Shanying Zhu

Abstract: This paper investigates the safety-aware task scheduling problem of real-time control systems in the presence of burst computing tasks. The basic idea is to adaptively release resources from low-criticality control tasks, ensuring timely completion of burst computing tasks and keeping the control system state within a safe threshold. An efficient algorithm called ADP-Opt is proposed to solve the problem. It first decouples the complex original problem into multiple subproblems, which greatly reduces the search space and improves the solution efficiency. In the subproblem, safety is maintained by limiting the upper bound of the system state's deviation to below the threshold. By introducing a penalty term in the cost function related to computing resources, we aim to balance the impact of control computing tasks on control performance and the consumption of computing resources. Consequently, the overall cost of the control system can be minimized while releasing enough resources for burst computing tasks. Simulation results show that the scheduling decisions made by ADP-Opt approach the optimal decision and maintain the safety of the control systems.

External IDs:doi:10.1109/ticps.2025.3537961