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2009
IEEE

Predicting Web Server Crashes: A Case Study in Comparing Prediction Algorithms

10 years 6 months ago
Predicting Web Server Crashes: A Case Study in Comparing Prediction Algorithms
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. Keywords—Dependability, High-Availability, ...
Javier Alonso, Jordi Torres, Ricard Gavaldà
Added 21 May 2010
Updated 21 May 2010
Type Conference
Year 2009
Where ICAS
Authors Javier Alonso, Jordi Torres, Ricard Gavaldà
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