Detecting errors in the ATLAS TDAQ system: a neural networks and support vector machines approach
Sloper, John Erik and Hines, Evor (2009) Detecting errors in the ATLAS TDAQ system: a neural networks and support vector machines approach. In: IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, Hong Kong, People's Republic of China, May 11-13, 2009. Published in: 2009 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications pp. 252-257.Full text not available from this repository.
Official URL: http://dx.doi.org/10.1109/CIMSA.2009.5069960
This paper describes how neural networks and support vector machines can be used to detect errors in a large scale distributed system, specifically the ATLAS Trigger and Data AcQuisition (TDAQ) system. By collecting, analysing and preprocessing some of the data available in the system it is possible to recognize and/or predict error situations arising in the system. This can be done without detailed knowledge of the system, nor of the data available. Hence the presented methods could be used in similar system without significant changes.
The TDAQ system, and in particular the main components related to this work, is described together with the test setup used. We simulate a number of error situations in the system and simultaneously gather both performance measures and error messages from the system. The data are then preprocessed and neural networks and support vector machines are applied to try to detect the error situations, achieving classification accuracy ranging from 88% to 100% for the neural networks and 90.8% to a 100% for the support vector machines approach.
|Item Type:||Conference Item (Paper)|
|Subjects:||Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
T Technology > TK Electrical engineering. Electronics Nuclear engineering
|Divisions:||Faculty of Science > Engineering|
|Journal or Publication Title:||2009 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications|
|Number of Pages:||6|
|Page Range:||pp. 252-257|
|Access rights to Published version:||Restricted or Subscription Access|
|Conference Paper Type:||Paper|
|Title of Event:||IEEE International Conference on Computational Intelligence for Measurement Systems and Applications|
|Type of Event:||Conference|
|Location of Event:||Hong Kong, People's Republic of China|
|Date(s) of Event:||May 11-13, 2009|
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