Feedback-based admission control for hard real-time task allocation under dynamic workload on many-core systems

Piotr Dziurzanski*, Amit Kumar Singh, Leandro Soares Indrusiak

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In hard real-time systems, a computationally expensive schedulability analysis has to be performed for every task. Fulfilling this requirement is particularly tough when system workload and service capacity are not available a priori and thus the analysis has to be conducted at runtime. This paper presents an approach for applying controltheory-based admission control to predict the task schedulability so that the exact schedulability analysis is performed only to the tasks with positive prediction results. In case of a careful fine-tuning of parameters, the proposed approach can be successfully applied even to many-core embedded systems with hard real-time constraints and other time-critical systems. The provided experimental results demonstrate that, on average, only 62% of the schedulability tests have to be performed in comparison with the traditional, open-loop approach. The proposed approach is particularly beneficial for heavier workloads, where the number of executed tasks is almost unchanged in comparison with the traditional open-loop approach. By our approach, only 32% of exact schedulability tests have to be conducted. Moreover, for the analysed industrial workloads with dependent jobs, the proposed technique admitted and executed 11% more tasks while not violating any timing constraints.

Original languageEnglish
Title of host publicationArchitecture of Computing Systems -- ARCS 2016
Subtitle of host publication29th International Conference, Nuremberg, Germany, April 4-7, 2016, Proceedings
PublisherSpringer
Pages157-169
Number of pages13
ISBN (Electronic)978-3-319-30695-7
ISBN (Print)9783319306940
DOIs
Publication statusPublished - 26 Mar 2016
Event29th International Conference on Architecture of Computing Systems, ARCS 2016 - Nuremberg, Germany
Duration: 4 Apr 20167 Apr 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9637
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference29th International Conference on Architecture of Computing Systems, ARCS 2016
Country/TerritoryGermany
CityNuremberg
Period4/04/167/04/16

Bibliographical note

© Springer International Publishing Switzerland. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details

Cite this