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Digital archive of theses discussed at the University of Pisa

 

Thesis etd-01282023-143227


Thesis type
Tesi di laurea magistrale
Author
RISPO, VERONICA
URN
etd-01282023-143227
Thesis title
Scheduling and Response-Time Analysis of Parallel Real-Time Tasks for Symmetric Multicores
Department
INGEGNERIA DELL'INFORMAZIONE
Course of study
EMBEDDED COMPUTING SYSTEMS
Supervisors
relatore Prof. Biondi, Alessandro
relatore Dott. Casini, Daniel
relatore Dott. Aromolo, Federico
Keywords
  • response-time analysis
  • schedulability analysis
  • parallel tasks
  • real-time systems
  • gang scheduling
  • partitioned scheduling
  • symmetric multicores
Graduation session start date
17/02/2023
Availability
Withheld
Release date
17/02/2093
Summary
The study of parallel task models for real-time systems has become fundamental due to the increasing computational demand of modern applications, which are executed in parallel to leverage the availability of multiple cores of multicore computing platforms and to boost performance.
In this context, the gang scheduling paradigm is receiving increasing attention thanks to the performance improvements it can provide for tightly-synchronized parallel applications. Existing works on real-time gang partitioned scheduling use a rigid model, where the number of cores required by a task is assumed to be constant, thus overestimating its computational demand. On the other hand, the bundled model, where tasks consist of segments (or bundles), each requiring a different number of cores, was introduced to obtain a more accurate representation of the tasks' parallelism. However, this model has only been analyzed for global scheduling, which is notably considered less predictable from the perspective of timing.
To fill this gap, this thesis presents an analysis method for parallel real-time tasks under fixed-priority partitioned scheduling as well as the gang scheduling paradigms.
In particular, two schedulability analysis methods are proposed, one based on a closed-form formulation and the other based on an optimization technique. In addition, specialized partitioning heuristics are introduced. Finally, the results of an experimental evaluation are presented, comparing the proposed methods and considering different allocation heuristics.
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