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Using Artificial Neural Networks for Admission Control in Firm Real-Time Systems
University of Skövde, Department of Computer Science.
2000 (English)Independent thesis Basic level (degree of Bachelor)Student thesis
Abstract [en]

Admission controllers in dynamic real-time systems perform traditional schedulability tests in order to determine whether incoming tasks will meet their deadlines. These tests are computationally expensive and typically run in n * log n time where n is the number of tasks in the system. An incoming task might therefore miss its deadline while the schedulability test is being performed, when there is a heavy load on the system. In our work we evaluate a new approach for admission control in firm real-time systems. Our work shows that ANNs can be used to perform a schedulability test in order to work as an admission controller in firm real-time systems. By integrating the ANN admission controller to a real-time simulator we show that our approach provides feasible performance compared to a traditional approach. The ANNs are able to make up to 86% correct admission decisions in our simulations and the computational cost of our ANN schedulability test has a constant value independent of the load of the system. Our results also show that the computational cost of a traditional approach increases as a function of n log n where n is the number of tasks in the system.

Place, publisher, year, edition, pages
Skövde: Institutionen för datavetenskap , 2000. , 47 p.
Keyword [en]
Firm Real-Time Systems, Overloads, Artificial Neural Networks, Admission Controller.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:his:diva-412OAI: oai:DiVA.org:his-412DiVA: diva2:2784
Presentation
(English)
Uppsok
Technology
Supervisors
Available from: 2007-12-19 Created: 2007-12-19 Last updated: 2009-10-05

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf