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Predicting Transient Overloads in Real-Time Systems using Artificial Neural Networks
University of Skövde, Department of Computer Science.
1999 (English)Independent thesis Advanced level (degree of Master (One Year))Student thesis
Abstract [en]

The emerging need for dynamically scheduled real-time systems requires methods for handling transient overloads. Current methods have in common that they deal with transient overloads as they occur, which gives the real-time system limited time to react to the overload. In this work we enable new approaches to overload management. Our work shows that artificial neural networks (ANNs) can predict future transient overloads. This way the real-time system can prepare for a transient overload before it actually occurs. Even though the artificial neural network is not yet integrated into any system, the results show that ANNs are able to satisfactory distinguish different workload scenarios into those that cause future overloads from those that do not. Two ANN architectures have been evaluated, one standard feed-forward ANN and one recurrent ANN. These ANNs were trained and tested on sporadic workloads with different average arrival rates. At best the ANNs are able to predict up to 85% of the transient overloads in the test workload, while causing around 10% false alarms.

Place, publisher, year, edition, pages
Skövde: Institutionen för datavetenskap , 1999. , 96 p.
Keyword [en]
Overload management, dynamic real-time systems, artificial neural networks
National Category
Information Science
Identifiers
URN: urn:nbn:se:his:diva-392OAI: oai:DiVA.org:his-392DiVA: diva2:2762
Presentation
(English)
Uppsok
Social and Behavioural Science, Law
Supervisors
Available from: 2007-12-12 Created: 2007-12-12 Last updated: 2010-02-10

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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