Event-driven scheduling for dynamic workload scaling in uniprocessor embedded systems

Li-Pin Chang*

*Corresponding author for this work

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

1 Scopus citations

Abstract

Many embedded systems are designed to take timely reactions to the occurrences of interested scenarios. Sometimes transient overloads might be experienced due to hardware malfunctions or workload bursts. Thus a mechanism to focus system attention on urgent events could be a key to provide reasonably stable service. In this paper, we propose a new approach for workload scaling in uniprocessor real-time embedded systems. A deterministic algorithm is adopted to selectively fed hardware events into a system, and an event-driven task model is introduced to formulate complex precedence constraints among tasks. Such a new approach removes the need for the adjustments of task periods and task phasing, which is crucial for many time-driven systems. The proposed approach was implemented in a real-time surveillance system, for which good accuracy and responsiveness were obtained under stressing workloads.

Original languageEnglish
Title of host publicationApplied Computing 2006 - The 21st Annual ACM Symposium on Applied Computing - Proceedings of the 2006 ACM Symposium on Applied Computing
Pages1462-1466
Number of pages5
DOIs
StatePublished - 21 Nov 2006
Event2006 ACM Symposium on Applied Computing - Dijon, France
Duration: 23 Apr 200627 Apr 2006

Publication series

NameProceedings of the ACM Symposium on Applied Computing
Volume2

Conference

Conference2006 ACM Symposium on Applied Computing
CountryFrance
CityDijon
Period23/04/0627/04/06

Keywords

  • Adaptive systems
  • Embedded systems
  • Overload management
  • Real-time systems

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    Chang, L-P. (2006). Event-driven scheduling for dynamic workload scaling in uniprocessor embedded systems. In Applied Computing 2006 - The 21st Annual ACM Symposium on Applied Computing - Proceedings of the 2006 ACM Symposium on Applied Computing (pp. 1462-1466). (Proceedings of the ACM Symposium on Applied Computing; Vol. 2). https://doi.org/10.1145/1141277.1141618