Semi-Partitioned Scheduling of Dynamic Real-Time Workload: A Practical Approach Based on Analysis-Driven Load Balancing

Authors Daniel Casini, Alessandro Biondi, Giorgio Buttazzo

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Daniel Casini
Alessandro Biondi
Giorgio Buttazzo

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Daniel Casini, Alessandro Biondi, and Giorgio Buttazzo. Semi-Partitioned Scheduling of Dynamic Real-Time Workload: A Practical Approach Based on Analysis-Driven Load Balancing. In 29th Euromicro Conference on Real-Time Systems (ECRTS 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 76, pp. 13:1-13:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)


Recent work showed that semi-partitioned scheduling can achieve near-optimal schedulability performance, is simpler to implement compared to global scheduling, and less heavier in terms of runtime overhead, thus resulting in an excellent choice for implementing real-world systems. However, semi-partitioned scheduling typically leverages an off-line design to allocate tasks across the available processors, which requires a-priori knowledge of the workload. Conversely, several simple global schedulers, as global earliest-deadline first (G-EDF), can transparently support dynamic workload without requiring a task-allocation phase. Nonetheless, such schedulers exhibit poor worst-case performance. This work proposes a semi-partitioned approach to efficiently schedule dynamic real-time workload on a multiprocessor system. A linear-time approximation for the C=D splitting scheme under partitioned EDF scheduling is first presented to reduce the complexity of online scheduling decisions. Then, a load-balancing algorithm is proposed for admitting new real-time workload in the system with limited workload re-allocation. A large-scale experimental study shows that the linear-time approximation has a very limited utilization loss compared to the exact technique and the proposed approach achieves very high schedulability performance, with a consistent improvement on G-EDF and pure partitioned EDF scheduling.
  • Semi-partitioned scheduling
  • dynamic workload
  • real-time


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