Harmonicity-Aware Task Partitioning for Fixed Priority Scheduling of Probabilistic Real-Time Tasks on Multi-Core Platforms

Abstract

The uncertainty due to performance variations of IC chips and resource sharing on multi-core platforms have significantly degraded the predictability of real-time systems. Traditional deterministic approaches based on the worst-case assumptions become extremely pessimistic and thus unpractical. In this article, we address the problem of scheduling a set of fixed-priority periodic real-time tasks on multi-core platforms in a probabilistic manner. Specifically, we consider task execution time as a probabilistic distribution and study how to schedule these tasks on multi-core platforms with guaranteed Quality of Service (QoS) requirements in terms of deadline-missing probabilities. Moreover, it is a well-known fact that the relationship among task periods, if exploited appropriately, can significantly improve the processor utilization. To this end, we present a novel approach to partition real-time tasks that can take both task execution time distributions and their period relationships into consideration. From our extensive experiment results, our proposed methods can greatly improve the schedulability of real-time tasks when compared with existing approaches.