Abstractions for aperiodic multiprocessor scheduling of real-time stream processing applications

J.P.H.M. Hausmans

Research output: ThesisPhD Thesis - Research UT, graduation UTAcademic

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Abstract

Embedded multiprocessor systems are often used in the domain of real-time stream processing applications to keep up with increasing power and performance requirements. Examples of such real-time stream processing applications are digital radio baseband processing and WLAN transceivers. These stream processing applications often have a dynamic character. For example the execution times and execution rates of the tasks of the stream processing applications vary and can even be data dependent. To cope with this dynamic behavior, the tasks are executed on the multiprocessor system in a data-driven fashion on run-time scheduled resources. Another important aspect of real-time stream processing applications are their strict performance constraints. A periodic source or sink imposes a throughput constraint and also latency constraints are common. For stream processing applications, violating these constraints typically leads to a major reduction of the quality of service of the applications. To prevent such violations of the temporal constraints, analysis methods are used. These analysis methods ease the processes of dimensioning, programming and optimizing the multiprocessor systems within these temporal constraints. Analysis methods rely on accurate abstractions of the analyzed applications. However, current abstractions have a limited accuracy and applicability and do therefore not always suffice. In this thesis we will present abstractions for multiprocessor systems in which the tasks are executed in a data-driven fashion and in which they have aperiodic schedules. These aperiodic schedules can capture the dynamic behavior of the real-time stream processing applications. We present accurate abstractions based on dataflow analysis techniques which can be used for a large class of multiprocessor systems. Compared to state of the art, we broaden the scope of dataflow analysis techniques, improve their accuracy and provide a new higher level of abstraction.
Original languageUndefined
Awarding Institution
  • University of Twente
Supervisors/Advisors
  • Bekooij, Marco Jan Gerrit, Supervisor
Thesis sponsors
Award date24 Apr 2015
Place of PublicationEnschede
Publisher
Print ISBNs978-90-365-3853-4
DOIs
Publication statusPublished - 24 Apr 2015

Keywords

  • Parallelism
  • Refinement
  • Stream Processing
  • Real Time
  • METIS-310323
  • Compositional Model
  • Abstraction
  • Static priority scheduling
  • EWI-25933
  • Run-time scheduling
  • data flow analysis
  • Data-driven
  • Temporal Analysis
  • IR-95644

Cite this

Hausmans, J.P.H.M.. / Abstractions for aperiodic multiprocessor scheduling of real-time stream processing applications. Enschede : Universiteit Twente, 2015. 206 p.
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Abstractions for aperiodic multiprocessor scheduling of real-time stream processing applications. / Hausmans, J.P.H.M.

Enschede : Universiteit Twente, 2015. 206 p.

Research output: ThesisPhD Thesis - Research UT, graduation UTAcademic

TY - THES

T1 - Abstractions for aperiodic multiprocessor scheduling of real-time stream processing applications

AU - Hausmans, J.P.H.M.

PY - 2015/4/24

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N2 - Embedded multiprocessor systems are often used in the domain of real-time stream processing applications to keep up with increasing power and performance requirements. Examples of such real-time stream processing applications are digital radio baseband processing and WLAN transceivers. These stream processing applications often have a dynamic character. For example the execution times and execution rates of the tasks of the stream processing applications vary and can even be data dependent. To cope with this dynamic behavior, the tasks are executed on the multiprocessor system in a data-driven fashion on run-time scheduled resources. Another important aspect of real-time stream processing applications are their strict performance constraints. A periodic source or sink imposes a throughput constraint and also latency constraints are common. For stream processing applications, violating these constraints typically leads to a major reduction of the quality of service of the applications. To prevent such violations of the temporal constraints, analysis methods are used. These analysis methods ease the processes of dimensioning, programming and optimizing the multiprocessor systems within these temporal constraints. Analysis methods rely on accurate abstractions of the analyzed applications. However, current abstractions have a limited accuracy and applicability and do therefore not always suffice. In this thesis we will present abstractions for multiprocessor systems in which the tasks are executed in a data-driven fashion and in which they have aperiodic schedules. These aperiodic schedules can capture the dynamic behavior of the real-time stream processing applications. We present accurate abstractions based on dataflow analysis techniques which can be used for a large class of multiprocessor systems. Compared to state of the art, we broaden the scope of dataflow analysis techniques, improve their accuracy and provide a new higher level of abstraction.

AB - Embedded multiprocessor systems are often used in the domain of real-time stream processing applications to keep up with increasing power and performance requirements. Examples of such real-time stream processing applications are digital radio baseband processing and WLAN transceivers. These stream processing applications often have a dynamic character. For example the execution times and execution rates of the tasks of the stream processing applications vary and can even be data dependent. To cope with this dynamic behavior, the tasks are executed on the multiprocessor system in a data-driven fashion on run-time scheduled resources. Another important aspect of real-time stream processing applications are their strict performance constraints. A periodic source or sink imposes a throughput constraint and also latency constraints are common. For stream processing applications, violating these constraints typically leads to a major reduction of the quality of service of the applications. To prevent such violations of the temporal constraints, analysis methods are used. These analysis methods ease the processes of dimensioning, programming and optimizing the multiprocessor systems within these temporal constraints. Analysis methods rely on accurate abstractions of the analyzed applications. However, current abstractions have a limited accuracy and applicability and do therefore not always suffice. In this thesis we will present abstractions for multiprocessor systems in which the tasks are executed in a data-driven fashion and in which they have aperiodic schedules. These aperiodic schedules can capture the dynamic behavior of the real-time stream processing applications. We present accurate abstractions based on dataflow analysis techniques which can be used for a large class of multiprocessor systems. Compared to state of the art, we broaden the scope of dataflow analysis techniques, improve their accuracy and provide a new higher level of abstraction.

KW - Parallelism

KW - Refinement

KW - Stream Processing

KW - Real Time

KW - METIS-310323

KW - Compositional Model

KW - Abstraction

KW - Static priority scheduling

KW - EWI-25933

KW - Run-time scheduling

KW - data flow analysis

KW - Data-driven

KW - Temporal Analysis

KW - IR-95644

U2 - 10.3990/1.9789036538534

DO - 10.3990/1.9789036538534

M3 - PhD Thesis - Research UT, graduation UT

SN - 978-90-365-3853-4

PB - Universiteit Twente

CY - Enschede

ER -