Generalized Elastic Scheduling for Real-Time Systems

Master's Thesis


The elastic task model is a powerful model for adapting periodic real-time systems in the presence of uncertainty. This thesis generalizes the existing elastic scheduling approach in several directions. First, it presents a general framework, which formulates a trade-off between task schedulability and a specific performance metric as an optimization problem. Such a framework allows real-time systems under overloads to graciously adapt by adjusting their performance level.

Second, it is shown in this thesis that the well-known task compression algorithm in fact solves a quadratic programming problem that seeks to minimize the sum of the squared deviation of a task’s utilization from initial desired utilization. This finding indicates that the task compression algorithm may be applied to efficiently solve other similar types of problems that often arise in real-time applications. In particular, an iterative approach is proposed to solve the period selection problem for real-time tasks with deadlines less than respective periods. Further, the framework is adapted to solve the deadline selection problem, which is useful in some real-time control systems with fixed periods.


Attribute NameValues
  • etd-04082008-091353

Author Thidapat Chantem
Advisor Dr. Sharon Hu
Contributor Dr. Christian Poellabauer, Committee Member
Contributor Dr. Sharon Hu, Committee Chair
Contributor Dr. Michael Lemmon, Committee Member
Degree Level Master's Thesis
Degree Discipline Computer Science and Engineering
Degree Name MSCSE
Defense Date
  • 2008-03-28

Submission Date 2008-04-08
  • United States of America

  • Real-time systems

  • embedded systems

  • system performance

  • real-time scheduling

  • University of Notre Dame

  • English

Record Visibility Public
Content License
  • All rights reserved

Departments and Units


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