Web server's reliability improvements using recurrent neural networks

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2012

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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
Original languageEnglish
Title of host publicationAdvances in Safety, Reliability and Risk Management : Proceedings Of The European Safety And Reliability Conference, Esrel 2011, Troyes, France, 18–22 September 2011
EditorsChristophe Bérenguer, Antoine Grall, C. Guedes Soares
Place of PublicationLondon
PublisherTaylor & Francis
Publication date2012
ISBN (print)978-0-415-68379-1
StatePublished - 2012
Event20th European Safety and Reliability Conference - Troyes, France


Conference20th European Safety and Reliability Conference
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