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Decision-driven scheduling

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Abstract

This paper presents a scheduling model, called decision-driven scheduling, elaborates key optimality results for a fundamental scheduling model, and evaluates new heuristics solving more general versions of the problem. In the context of applications that need control and actuation, the traditional execution model has often been either time-driven or event-driven. In time-driven applications, sensors are sampled periodically, leading to the classical periodic task model. In event-driven applications, sensors are sampled when an event of interest occurs, such as motion-activated cameras, leading to an event-driven task activation model. In contrast, in decision-driven applications, sensors are sampled when a particular decision must be made. We offer a justification for why decision-driven scheduling might be of increasing interest to Internet-of-things applications, and explain why it leads to interesting new scheduling problems (unlike time-driven and event-driven scheduling), including the problems addressed in this paper.

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Notes

  1. Note that, for a moving vehicle, the communication channel is wireless. In another example, such as collecting data at a command center, the channel may be wired. To keep the discussion simple, in this paper, we abstract away from the underlying channel technology. We consider a single bottleneck resource.

  2. This does not mean that the inter-arrival times are fixed. The arrivals of decision tasks still follow the Poisson process with the specified arrival rate.

  3. Fig. 13 is shown separately from Fig. 12 since RateFirst is irrelevant to Rtdb.

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Acknowledgements

This work is supported in part by Grants from US ARL W911NF-09-2-0053, Navy N00014-16-1-2151, ONR N00014-14-1-0717, and NSF CNS 13-02563, 13-29886, and 16-18627. Any opinions, findings, and conclusions or recommendations expressed here are those of the authors and do not necessarily reflect the views of sponsors.

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Authors and Affiliations

  1. Department of Computer Science, Yale University, New Haven, CT, USA

    Jung-Eun Kim

  2. Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, IL, USA

    Tarek Abdelzaher & Lui Sha

  3. City University of New York, New York, NY, USA

    Amotz Bar-Noy

  4. Multilingual Computing & Analytics Branch, U.S. Army Research Laboratory, Adelphi, MD, 20783, USA

    Reginald L. Hobbs

  5. Raytheon BBN Technologies, Cambridge, MA, 02138, USA

    William Dron

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  1. Jung-Eun Kim
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  2. Tarek Abdelzaher
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  3. Lui Sha
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  4. Amotz Bar-Noy
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Correspondence to Jung-Eun Kim.

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Following an earlier conference publication, this paper presents a novel scheduling model, called decision-driven scheduling, elaborates key optimality results derived in the earlier publication for that scheduling model, and evaluates new heuristics solving more general versions of the problem.

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Kim, JE., Abdelzaher, T., Sha, L. et al. Decision-driven scheduling. Real-Time Syst 55, 514–551 (2019). https://doi.org/10.1007/s11241-018-09324-6

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