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Optimal Dataflow Scheduling on a Heterogeneous Multiprocessor With Reduced Response Time Bounds

Authors Zheng Dong, Cong Liu, Alan Gatherer, Lee McFearin, Peter Yan, James H. Anderson



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Zheng Dong
Cong Liu
Alan Gatherer
Lee McFearin
Peter Yan
James H. Anderson

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Zheng Dong, Cong Liu, Alan Gatherer, Lee McFearin, Peter Yan, and James H. Anderson. Optimal Dataflow Scheduling on a Heterogeneous Multiprocessor With Reduced Response Time Bounds. In 29th Euromicro Conference on Real-Time Systems (ECRTS 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 76, pp. 15:1-15:22, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2017)
https://doi.org/10.4230/LIPIcs.ECRTS.2017.15

Abstract

Heterogeneous computing platforms with multiple types of computing resources have been widely used in many industrial systems to process dataflow tasks with pre-defined affinity of tasks to subgroups of resources. For many dataflow workloads with soft real-time requirements, guaranteeing fast and bounded response times is often the objective. This paper presents a new set of analysis techniques showing that a classical real-time scheduler, namely earliest-deadline first (EDF), is able to support dataflow tasks scheduled on such heterogeneous platforms with provably bounded response times while incurring no resource capacity loss, thus proving EDF to be an optimal solution for this scheduling problem. Experiments using synthetic workloads with widely varied parameters also demonstrate that the magnitude of the response time bounds yielded under the proposed analysis is reasonably small under all scenarios. Compared to the state-of-the-art soft real-time analysis techniques, our test yields a 68% reduction on response time bounds on average. This work demonstrates the potential of applying EDF into practical industrial systems containing dataflow-based workloads that desire guaranteed bounded response times.
Keywords
  • Real-time Scheduling
  • schedulability
  • heterogeneous multiprocessor

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