Value-Aware Real-Time Scheduling for Intelligent Transportation Systems

This paper presents a novel real-time scheduling technique tailored for sporadic task graphs in multi-processor systems, motivated by the computational demands of intelligent transportation systems (ITS). Unlike traditional deadline-based approaches, the proposed method prioritizes tasks based on their anticipated contribution to system utility, expressed as a value, under runtime uncertainty. Each request is associated with a reputation score, with progressively refined value estimations revealed through sequential task executions. A feasibility-aware value density metric is introduced to dynamically schedule tasks while respecting global deadlines and the non-preemptive nature of execution. Extensive simulations, conducted with 1, 2, and 4 processors, evaluate performance under varying traffic conditions and workload intensities. The results confirm that the proposed method significantly improves the total earned value, particularly under high-load scenarios or limited processing resources.