Probabilistic analysis for mixed criticality systems using fixed priority preemptive scheduling

Dorin Maxim; Robert I. Davis; Liliana Cucu-Grosjean; Arvind Easwaran

doi:10.1145/3139258.3139276

Probabilistic analysis for mixed criticality systems using fixed priority preemptive scheduling

Dorin Maxim, Robert I. Davis, Liliana Cucu-Grosjean, Arvind Easwaran

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

22 Citations (Scopus)

Abstract

This paper introduces probabilistic analysis for fixed priority preemptive scheduling of mixed criticality systems on a uniprocessor using the Adaptive Mixed Criticality (AMC) and Static Mixed Criticality (SMC) schemes. We compare this analysis to existing deterministic methods, highlighting the performance gains that can be obtained by utilising more detailed information about worst-case execution time estimates described in terms of probability distributions. Besides improvements in schedulability we also demonstrate significant gains in terms of the budgets that can be allocated to LO-criticality tasks.

Original language	English
Title of host publication	Proceedings of the 25th International Conference on Real-Time Networks and Systems, RTNS 2017
Publisher	Association for Computing Machinery
Pages	237-246
Number of pages	10
ISBN (Electronic)	9781450352864
DOIs	https://doi.org/10.1145/3139258.3139276
Publication status	Published - Oct 4 2017
Externally published	Yes
Event	25th International Conference on Real-Time Networks and Systems, RTNS 2017 - Grenoble, France Duration: Oct 4 2017 → Oct 6 2017

Publication series

Name	ACM International Conference Proceeding Series
Volume	Part F131837

Conference

Conference	25th International Conference on Real-Time Networks and Systems, RTNS 2017
Country/Territory	France
City	Grenoble
Period	10/4/17 → 10/6/17

Bibliographical note

Publisher Copyright:
© 2017 ACM.

ASJC Scopus Subject Areas

Software
Human-Computer Interaction
Computer Vision and Pattern Recognition
Computer Networks and Communications

Keywords

Fixed priority
Mixed criticality
Probabilities
Real-Time systems
Schedulability analysis

Access to Document

10.1145/3139258.3139276

Cite this

Maxim, D., Davis, R. I., Cucu-Grosjean, L., & Easwaran, A. (2017). Probabilistic analysis for mixed criticality systems using fixed priority preemptive scheduling. In Proceedings of the 25th International Conference on Real-Time Networks and Systems, RTNS 2017 (pp. 237-246). (ACM International Conference Proceeding Series; Vol. Part F131837). Association for Computing Machinery. https://doi.org/10.1145/3139258.3139276

@inproceedings{31b8058499e04a789c4ddb31fad005a1,

title = "Probabilistic analysis for mixed criticality systems using fixed priority preemptive scheduling",

abstract = "This paper introduces probabilistic analysis for fixed priority preemptive scheduling of mixed criticality systems on a uniprocessor using the Adaptive Mixed Criticality (AMC) and Static Mixed Criticality (SMC) schemes. We compare this analysis to existing deterministic methods, highlighting the performance gains that can be obtained by utilising more detailed information about worst-case execution time estimates described in terms of probability distributions. Besides improvements in schedulability we also demonstrate significant gains in terms of the budgets that can be allocated to LO-criticality tasks.",

keywords = "Fixed priority, Mixed criticality, Probabilities, Real-Time systems, Schedulability analysis",

author = "Dorin Maxim and Davis, {Robert I.} and Liliana Cucu-Grosjean and Arvind Easwaran",

note = "Publisher Copyright: {\textcopyright} 2017 ACM.; 25th International Conference on Real-Time Networks and Systems, RTNS 2017 ; Conference date: 04-10-2017 Through 06-10-2017",

year = "2017",

month = oct,

day = "4",

doi = "10.1145/3139258.3139276",

language = "English",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery",

pages = "237--246",

booktitle = "Proceedings of the 25th International Conference on Real-Time Networks and Systems, RTNS 2017",

}

Maxim, D, Davis, RI, Cucu-Grosjean, L & Easwaran, A 2017, Probabilistic analysis for mixed criticality systems using fixed priority preemptive scheduling. in Proceedings of the 25th International Conference on Real-Time Networks and Systems, RTNS 2017. ACM International Conference Proceeding Series, vol. Part F131837, Association for Computing Machinery, pp. 237-246, 25th International Conference on Real-Time Networks and Systems, RTNS 2017, Grenoble, France, 10/4/17. https://doi.org/10.1145/3139258.3139276

Probabilistic analysis for mixed criticality systems using fixed priority preemptive scheduling. / Maxim, Dorin; Davis, Robert I.; Cucu-Grosjean, Liliana et al.
Proceedings of the 25th International Conference on Real-Time Networks and Systems, RTNS 2017. Association for Computing Machinery, 2017. p. 237-246 (ACM International Conference Proceeding Series; Vol. Part F131837).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Probabilistic analysis for mixed criticality systems using fixed priority preemptive scheduling

AU - Maxim, Dorin

AU - Davis, Robert I.

AU - Cucu-Grosjean, Liliana

AU - Easwaran, Arvind

PY - 2017/10/4

Y1 - 2017/10/4

N2 - This paper introduces probabilistic analysis for fixed priority preemptive scheduling of mixed criticality systems on a uniprocessor using the Adaptive Mixed Criticality (AMC) and Static Mixed Criticality (SMC) schemes. We compare this analysis to existing deterministic methods, highlighting the performance gains that can be obtained by utilising more detailed information about worst-case execution time estimates described in terms of probability distributions. Besides improvements in schedulability we also demonstrate significant gains in terms of the budgets that can be allocated to LO-criticality tasks.

AB - This paper introduces probabilistic analysis for fixed priority preemptive scheduling of mixed criticality systems on a uniprocessor using the Adaptive Mixed Criticality (AMC) and Static Mixed Criticality (SMC) schemes. We compare this analysis to existing deterministic methods, highlighting the performance gains that can be obtained by utilising more detailed information about worst-case execution time estimates described in terms of probability distributions. Besides improvements in schedulability we also demonstrate significant gains in terms of the budgets that can be allocated to LO-criticality tasks.

KW - Fixed priority

KW - Mixed criticality

KW - Probabilities

KW - Real-Time systems

KW - Schedulability analysis

UR - http://www.scopus.com/inward/record.url?scp=85037347579&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85037347579&partnerID=8YFLogxK

U2 - 10.1145/3139258.3139276

DO - 10.1145/3139258.3139276

M3 - Conference contribution

AN - SCOPUS:85037347579

T3 - ACM International Conference Proceeding Series

SP - 237

EP - 246

BT - Proceedings of the 25th International Conference on Real-Time Networks and Systems, RTNS 2017

PB - Association for Computing Machinery

T2 - 25th International Conference on Real-Time Networks and Systems, RTNS 2017

Y2 - 4 October 2017 through 6 October 2017

ER -

Maxim D, Davis RI, Cucu-Grosjean L, Easwaran A. Probabilistic analysis for mixed criticality systems using fixed priority preemptive scheduling. In Proceedings of the 25th International Conference on Real-Time Networks and Systems, RTNS 2017. Association for Computing Machinery. 2017. p. 237-246. (ACM International Conference Proceeding Series). doi: 10.1145/3139258.3139276

Probabilistic analysis for mixed criticality systems using fixed priority preemptive scheduling

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus Subject Areas

Keywords

Access to Document

Other files and links

Fingerprint

Cite this