Temporal analysis of static priority preemptive scheduled cyclic streaming applications using CSDF models

Philip Sebastian Kurtin, Marco Jan Gerrit Bekooij

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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Abstract

Real-time streaming applications with cyclic data dependencies that are executed on multiprocessor systems with processor sharing usually require a temporal analysis to give guarantees on their temporal behavior at design time. Current accurate analysis techniques for cyclic applications that are scheduled with Static Priority Preemptive (SPP) schedulers are however limited to the analysis of applications that can be expressed with Homogeneous Synchronous Dataflow (HSDF) models, i.e. in which all tasks operate at a single rate. Moreover, it is required that both input and output buffers synchronize atomically at the beginnings and finishes of task executions, which is difficult to realize on many existing hardware platforms. This paper presents a temporal analysis approach for cyclic real-time streaming applications executed on multiprocessor systems with processor sharing and SPP scheduling that can be expressed using Cyclo-Static Dataflow (CSDF) models. This allows to model tasks with multiple phases and changing rates and furthermore resolves the problematic restriction that buffer synchronization must occur atomically at the boundaries of task executions. For that purpose a joint interference characterization over multiple phases is introduced, which realizes a significant accuracy improvement compared to an isolated consideration of interference. Applicability, efficiency and accuracy of the presented approach are evaluated in a case study using a WLAN 802.11p transceiver application. Thereby different use-cases of CSDF modeling are discussed, including a CSDF model relaxing the requirement of atomic synchronization.
Original languageUndefined
Title of host publication2016 ACM/IEEE Symposium on Embedded Systems for Real-Time Multimedia (ESTIMedia)
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Pages94-103
Number of pages10
ISBN (Print)978-1-4503-4543-9
DOIs
Publication statusPublished - 7 Oct 2016
Event14th ACM/IEEE Symposium on Embedded Systems for Real-Time Multimedia, ESTIMedia 2016 - Pittsburgh, United States
Duration: 6 Oct 20167 Oct 2016
Conference number: 14
http://www.estimedia.org/past/ESTIMedia2016/index.html

Publication series

Name
PublisherACM

Conference

Conference14th ACM/IEEE Symposium on Embedded Systems for Real-Time Multimedia, ESTIMedia 2016
Abbreviated titleESTIMedia
CountryUnited States
CityPittsburgh
Period6/10/167/10/16
Internet address

Keywords

  • IR-101816
  • METIS-318555
  • Real Time
  • Joint Interference Characterization
  • Processor Sharing
  • Multi-Rate Applications
  • Multi-Phase Tasks
  • Dataflow Modeling
  • MPSoCs
  • Abstraction
  • CSDF
  • Temporal Analysis
  • Streaming Applications
  • Static Priority Preemptive Scheduling
  • EWI-27311
  • Cyclic Applications

Cite this

Kurtin, P. S., & Bekooij, M. J. G. (2016). Temporal analysis of static priority preemptive scheduled cyclic streaming applications using CSDF models. In 2016 ACM/IEEE Symposium on Embedded Systems for Real-Time Multimedia (ESTIMedia) (pp. 94-103). New York: Association for Computing Machinery (ACM). https://doi.org/10.1145/2993452.2993564
Kurtin, Philip Sebastian ; Bekooij, Marco Jan Gerrit. / Temporal analysis of static priority preemptive scheduled cyclic streaming applications using CSDF models. 2016 ACM/IEEE Symposium on Embedded Systems for Real-Time Multimedia (ESTIMedia). New York : Association for Computing Machinery (ACM), 2016. pp. 94-103
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title = "Temporal analysis of static priority preemptive scheduled cyclic streaming applications using CSDF models",
abstract = "Real-time streaming applications with cyclic data dependencies that are executed on multiprocessor systems with processor sharing usually require a temporal analysis to give guarantees on their temporal behavior at design time. Current accurate analysis techniques for cyclic applications that are scheduled with Static Priority Preemptive (SPP) schedulers are however limited to the analysis of applications that can be expressed with Homogeneous Synchronous Dataflow (HSDF) models, i.e. in which all tasks operate at a single rate. Moreover, it is required that both input and output buffers synchronize atomically at the beginnings and finishes of task executions, which is difficult to realize on many existing hardware platforms. This paper presents a temporal analysis approach for cyclic real-time streaming applications executed on multiprocessor systems with processor sharing and SPP scheduling that can be expressed using Cyclo-Static Dataflow (CSDF) models. This allows to model tasks with multiple phases and changing rates and furthermore resolves the problematic restriction that buffer synchronization must occur atomically at the boundaries of task executions. For that purpose a joint interference characterization over multiple phases is introduced, which realizes a significant accuracy improvement compared to an isolated consideration of interference. Applicability, efficiency and accuracy of the presented approach are evaluated in a case study using a WLAN 802.11p transceiver application. Thereby different use-cases of CSDF modeling are discussed, including a CSDF model relaxing the requirement of atomic synchronization.",
keywords = "IR-101816, METIS-318555, Real Time, Joint Interference Characterization, Processor Sharing, Multi-Rate Applications, Multi-Phase Tasks, Dataflow Modeling, MPSoCs, Abstraction, CSDF, Temporal Analysis, Streaming Applications, Static Priority Preemptive Scheduling, EWI-27311, Cyclic Applications",
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Kurtin, PS & Bekooij, MJG 2016, Temporal analysis of static priority preemptive scheduled cyclic streaming applications using CSDF models. in 2016 ACM/IEEE Symposium on Embedded Systems for Real-Time Multimedia (ESTIMedia). Association for Computing Machinery (ACM), New York, pp. 94-103, 14th ACM/IEEE Symposium on Embedded Systems for Real-Time Multimedia, ESTIMedia 2016, Pittsburgh, United States, 6/10/16. https://doi.org/10.1145/2993452.2993564

Temporal analysis of static priority preemptive scheduled cyclic streaming applications using CSDF models. / Kurtin, Philip Sebastian; Bekooij, Marco Jan Gerrit.

2016 ACM/IEEE Symposium on Embedded Systems for Real-Time Multimedia (ESTIMedia). New York : Association for Computing Machinery (ACM), 2016. p. 94-103.

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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N2 - Real-time streaming applications with cyclic data dependencies that are executed on multiprocessor systems with processor sharing usually require a temporal analysis to give guarantees on their temporal behavior at design time. Current accurate analysis techniques for cyclic applications that are scheduled with Static Priority Preemptive (SPP) schedulers are however limited to the analysis of applications that can be expressed with Homogeneous Synchronous Dataflow (HSDF) models, i.e. in which all tasks operate at a single rate. Moreover, it is required that both input and output buffers synchronize atomically at the beginnings and finishes of task executions, which is difficult to realize on many existing hardware platforms. This paper presents a temporal analysis approach for cyclic real-time streaming applications executed on multiprocessor systems with processor sharing and SPP scheduling that can be expressed using Cyclo-Static Dataflow (CSDF) models. This allows to model tasks with multiple phases and changing rates and furthermore resolves the problematic restriction that buffer synchronization must occur atomically at the boundaries of task executions. For that purpose a joint interference characterization over multiple phases is introduced, which realizes a significant accuracy improvement compared to an isolated consideration of interference. Applicability, efficiency and accuracy of the presented approach are evaluated in a case study using a WLAN 802.11p transceiver application. Thereby different use-cases of CSDF modeling are discussed, including a CSDF model relaxing the requirement of atomic synchronization.

AB - Real-time streaming applications with cyclic data dependencies that are executed on multiprocessor systems with processor sharing usually require a temporal analysis to give guarantees on their temporal behavior at design time. Current accurate analysis techniques for cyclic applications that are scheduled with Static Priority Preemptive (SPP) schedulers are however limited to the analysis of applications that can be expressed with Homogeneous Synchronous Dataflow (HSDF) models, i.e. in which all tasks operate at a single rate. Moreover, it is required that both input and output buffers synchronize atomically at the beginnings and finishes of task executions, which is difficult to realize on many existing hardware platforms. This paper presents a temporal analysis approach for cyclic real-time streaming applications executed on multiprocessor systems with processor sharing and SPP scheduling that can be expressed using Cyclo-Static Dataflow (CSDF) models. This allows to model tasks with multiple phases and changing rates and furthermore resolves the problematic restriction that buffer synchronization must occur atomically at the boundaries of task executions. For that purpose a joint interference characterization over multiple phases is introduced, which realizes a significant accuracy improvement compared to an isolated consideration of interference. Applicability, efficiency and accuracy of the presented approach are evaluated in a case study using a WLAN 802.11p transceiver application. Thereby different use-cases of CSDF modeling are discussed, including a CSDF model relaxing the requirement of atomic synchronization.

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Kurtin PS, Bekooij MJG. Temporal analysis of static priority preemptive scheduled cyclic streaming applications using CSDF models. In 2016 ACM/IEEE Symposium on Embedded Systems for Real-Time Multimedia (ESTIMedia). New York: Association for Computing Machinery (ACM). 2016. p. 94-103 https://doi.org/10.1145/2993452.2993564