Conference paper
Web server's reliability improvements using recurrent neural networks
In this paper we describe an interesting approach to error prediction illustrated by experimental results. The application consists of monitoring the activity for the web servers in order to collect the specific data. Predicting an error with severe consequences for the performance of a server (the result of which is that its functionality becomes totally inaccessible or hard to access for clients) requires measuring the capacity of a server at any given time.
This measurement is highly complex, if not impossible. There are several variables which we can measure on a running system, such as: CPU usage, network usage and memory usage. We collect different data sets from monitoring the web server's activity and for each one we predict the server's reliability with the proposed recurrent neural network. © 2012 Taylor & Francis Group
Language: | English |
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Publisher: | Taylor & Francis |
Year: | 2012 |
Pages: | 441-444 |
Proceedings: | European Safety and Reliability Conference 2011 |
Journal subtitle: | Proceedings of the European Safety and Reliability Conference, Esrel 2011, Troyes, France, 18–22 September 2011 |
ISBN: | 0203135105 , 0415683793 , 0429217269 , 1136483640 , 1136483691 , 1280122943 , 1336099429 , 1466509716 , 9780203135105 , 9780415683791 , 9780429217265 , 9781136483646 , 9781136483691 , 9781280122941 , 9781336099425 and 9781466509719 |
Types: | Conference paper |
ORCIDs: | Madsen, Henrik |