Web server's reliability improvements using recurrent neural networks

Henrik Madsen, Rǎzvan-Daniel Albu, Ioan Felea, Albeanu Grigore, Florin Popentiu, Radu Catalin Ţarcǎ

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


    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
    Publication statusPublished - 2012
    Event20th European Safety and Reliability Conference: Advances in Safety, Reliability and Risk Management - Troyes, France
    Duration: 18 Sep 201122 Sep 2011
    Conference number: 20


    Conference20th European Safety and Reliability Conference
    Internet address


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