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Predicting web server crashes: a case study in comparing prediction algorithms
dc.contributor.author | Alonso López, Javier |
dc.contributor.author | Torres Viñals, Jordi |
dc.contributor.author | Gavaldà Mestre, Ricard |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics |
dc.date.accessioned | 2011-09-28T11:08:44Z |
dc.date.available | 2011-09-28T11:08:44Z |
dc.date.created | 2009 |
dc.date.issued | 2009 |
dc.identifier.citation | Alonso, J.; Torres, J.; Gavaldà, R. Predicting web server crashes: a case study in comparing prediction algorithms. A: International Conference on Autonomic and Autonomous Systems. "5th International Conference on Autonomic and Autonomous Systems". Valencia: IARIA, 2009, p. 264-269. |
dc.identifier.isbn | 978-0-7695-3584-5 |
dc.identifier.uri | http://hdl.handle.net/2117/13377 |
dc.description.abstract | Traditionally, performance has been the most important metrics when evaluating a system. However, in the last decades industry and academia have been paying increasing attention to another metric to evaluate servers: availability. A web server may serve many users when running, but if it is out of service too much time, it becomes useless and expensive. The industry has adopted several techniques to improve system availability, yet crashes still happen. In this paper, we propose a new framework to predict time-to-failure when the system is suffering transient failures that consume resources randomly. We study which machine learning algorithms build a more accurate model of the behavior of the anomaly system, and focus on Linear Regression and Decision Tree algorithms. Our preliminary results show that M5P (a Decision Tree algorithm) is the best option to model the behavior of the system under the random injection of memory leaks. |
dc.format.extent | 6 p. |
dc.language.iso | eng |
dc.publisher | IARIA |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic |
dc.subject.lcsh | Machine learning |
dc.subject.lcsh | Decision trees |
dc.subject.lcsh | Regression analysis |
dc.subject.other | Dependability |
dc.subject.other | High-availability |
dc.subject.other | Prediction algorithms |
dc.title | Predicting web server crashes: a case study in comparing prediction algorithms |
dc.type | Conference report |
dc.subject.lemac | Aprenentatge automàtic |
dc.subject.lemac | Arbres de decisió |
dc.subject.lemac | Anàlisi de regressió |
dc.contributor.group | Universitat Politècnica de Catalunya. CAP - Grup de Computació d'Altes Prestacions |
dc.contributor.group | Universitat Politècnica de Catalunya. LARCA - Laboratori d'Algorísmia Relacional, Complexitat i Aprenentatge |
dc.identifier.doi | 10.1109/ICAS.2009.56 |
dc.rights.access | Open Access |
local.identifier.drac | 2403009 |
dc.description.version | Postprint (published version) |
local.citation.author | Alonso, J.; Torres, J.; Gavaldà, R. |
local.citation.contributor | International Conference on Autonomic and Autonomous Systems |
local.citation.pubplace | Valencia |
local.citation.publicationName | 5th International Conference on Autonomic and Autonomous Systems |
local.citation.startingPage | 264 |
local.citation.endingPage | 269 |