On-line scheduling of target sensitive periodic tasks with the gravitational task model

Target sensitive tasks have an execution window for feasibility and must execute at a target point in time for maximum utility. In the gravitational task model, a task can express a target point, and the utility decay as a function of the deviation from this point. A method called equilibrium approx...

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Veröffentlicht in:Proceedings of the Conference on Design, Automation and Test in Europe S. 578 - 581
Hauptverfasser: Guerra, Raphael, Fohler, Gerhard
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: San Jose, CA, USA EDA Consortium 12.03.2012
Schriftenreihe:ACM Conferences
Schlagworte:
Hardware > Hardware validation
Theory of computation > Design and analysis of algorithms > Approximation algorithms analysis > Scheduling algorithms
Theory of computation > Design and analysis of algorithms > Online algorithms > Online learning algorithms
Theory of computation > Design and analysis of algorithms > Online algorithms > Online learning algorithms > Scheduling algorithms
Theory of computation > Models of computation > Interactive computation
Theory of computation > Theory and algorithms for application domains > Machine learning theory > Online learning theory
Theory of computation > Theory and algorithms for application domains > Machine learning theory > Reinforcement learning > Sequential decision making
ISBN:3981080181, 9783981080186
Online-Zugang:Volltext
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Zusammenfassung:Target sensitive tasks have an execution window for feasibility and must execute at a target point in time for maximum utility. In the gravitational task model, a task can express a target point, and the utility decay as a function of the deviation from this point. A method called equilibrium approximates the schedule with maximum utility accrual based on an analogy with physical pendulums. In this paper, we propose a scheduling algorithm for this task model to schedule periodic tasks. The basic idea of our solution is to combine the equilibrium with Earliest Deadline First (EDF) in order to reuse EDF's well studied timeliness analysis. We present simulation results and an example multimedia application to show the benefits of our solution.
ISBN:3981080181
9783981080186
DOI:10.5555/2492708.2492852