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Adaptive on-line software aging prediction based on Machine Learning Javier Alonso and Jordi Torres
 

Summary: Adaptive on-line software aging prediction based on Machine Learning
Javier Alonso and Jordi Torres
Barcelona Supercomputing Center
Dept. of Computer Architecture
Technical University of Catalonia
{alonso,torres}@ac.upc.edu
Josep Ll. Berral and Ricard Gavald`a
Dept. of Software
Technical University of Catalonia
{jlberral,gavalda}@lsi.upc.edu
Abstract
The growing complexity of software systems is resulting
in an increasing number of software faults. According to
the literature, software faults are becoming one of the main
sources of unplanned system outages, and have an impor-
tant impact on company benefits and image. For this rea-
son, a lot of techniques (such as clustering, fail-over tech-
niques, or server redundancy) have been proposed to avoid
software failures, and yet they still happen. Many software
failures are those due to the software aging phenomena. In

  

Source: Alonso, Javier - Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya

 

Collections: Computer Technologies and Information Sciences