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Web server’s reliability improvements using recurrent neural networks

Domenii publicaţii > Ştiinţe informatice + Tipuri publicaţii > Articol în volumul unei conferinţe

Autori: Madsen H., R.-D. Albu, I. Felea, G. Albeanu, Fl. Popentiu-Vladicescu, R. C. Tarca

Editorial: Bérenguer, Grall & Guedes Soares, Taylor & Francis Group, London, ISBN 978-0-415-68379-1, Advances in Safety, Reliability and Risk Management, p.2685-2690, 2012.

Rezumat:

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.

Cuvinte cheie: web server's reliability, recurrent neural networks // web server's reliability, recurrent neural networks