Decision-driven scheduling

Jung Eun Kim, Tarek Abdelzaher, Lui Sha, Amotz Bar-Noy, Reginald L. Hobbs, William Dron

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)514-551
Number of pages38
JournalReal-Time Systems
Volume55
Issue number3
DOIs
StatePublished - Jul 15 2019

Fingerprint

Event-driven
Scheduling
Sensor
Periodic Tasks
Sensors
Internet of Things
Task Model
Decision Model
Justification
Scheduling Problem
Activation
Optimality
Camera
Model
Heuristics
Chemical activation
Cameras
Motion
Evaluate

Keywords

  • Decision-driven
  • Disaster response infrastructure
  • Freshness
  • Internet of Things
  • Smart cities

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Computer Science Applications
  • Computer Networks and Communications
  • Control and Optimization
  • Electrical and Electronic Engineering

Cite this

Kim, J. E., Abdelzaher, T., Sha, L., Bar-Noy, A., Hobbs, R. L., & Dron, W. (2019). Decision-driven scheduling. Real-Time Systems, 55(3), 514-551. https://doi.org/10.1007/s11241-018-09324-6

Decision-driven scheduling. / Kim, Jung Eun; Abdelzaher, Tarek; Sha, Lui; Bar-Noy, Amotz; Hobbs, Reginald L.; Dron, William.

In: Real-Time Systems, Vol. 55, No. 3, 15.07.2019, p. 514-551.

Research output: Contribution to journalArticle

Kim, JE, Abdelzaher, T, Sha, L, Bar-Noy, A, Hobbs, RL & Dron, W 2019, 'Decision-driven scheduling', Real-Time Systems, vol. 55, no. 3, pp. 514-551. https://doi.org/10.1007/s11241-018-09324-6
Kim, Jung Eun ; Abdelzaher, Tarek ; Sha, Lui ; Bar-Noy, Amotz ; Hobbs, Reginald L. ; Dron, William. / Decision-driven scheduling. In: Real-Time Systems. 2019 ; Vol. 55, No. 3. pp. 514-551.
@article{083d8d5ad59b4a79a0e16bcd8f0a219b,
title = "Decision-driven scheduling",
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.",
keywords = "Decision-driven, Disaster response infrastructure, Freshness, Internet of Things, Smart cities",
author = "Kim, {Jung Eun} and Tarek Abdelzaher and Lui Sha and Amotz Bar-Noy and Hobbs, {Reginald L.} and William Dron",
year = "2019",
month = "7",
day = "15",
doi = "10.1007/s11241-018-09324-6",
language = "English (US)",
volume = "55",
pages = "514--551",
journal = "Real-Time Systems",
issn = "0922-6443",
publisher = "Springer Netherlands",
number = "3",

}

TY - JOUR

T1 - Decision-driven scheduling

AU - Kim, Jung Eun

AU - Abdelzaher, Tarek

AU - Sha, Lui

AU - Bar-Noy, Amotz

AU - Hobbs, Reginald L.

AU - Dron, William

PY - 2019/7/15

Y1 - 2019/7/15

N2 - 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.

AB - 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.

KW - Decision-driven

KW - Disaster response infrastructure

KW - Freshness

KW - Internet of Things

KW - Smart cities

UR - http://www.scopus.com/inward/record.url?scp=85058957427&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85058957427&partnerID=8YFLogxK

U2 - 10.1007/s11241-018-09324-6

DO - 10.1007/s11241-018-09324-6

M3 - Article

AN - SCOPUS:85058957427

VL - 55

SP - 514

EP - 551

JO - Real-Time Systems

JF - Real-Time Systems

SN - 0922-6443

IS - 3

ER -