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Using Artificial Neural Networks for Admission Control in Firm Real-Time Systems
Högskolan i Skövde, Institutionen för datavetenskap.
2000 (engelsk)Independent thesis Basic level (degree of Bachelor)Oppgave
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.

sted, utgiver, år, opplag, sider
Skövde: Institutionen för datavetenskap , 2000. , s. 47
Emneord [en]
Firm Real-Time Systems, Overloads, Artificial Neural Networks, Admission Controller.
HSV kategori
Identifikatorer
URN: urn:nbn:se:his:diva-412OAI: oai:DiVA.org:his-412DiVA, id: diva2:2784
Presentation
(engelsk)
Uppsök
Technology
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Tilgjengelig fra: 2007-12-19 Laget: 2007-12-19 Sist oppdatert: 2018-01-12

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