Self-healing Performance Anomalies in Web-based Applications



In this paper, we describe the SHõWA framework and evaluate its ability to recover from performance anomalies in Web-based applications. SHõWA is meant to automatically detect and recover from performance anomalies, without calling for human intervention. It does not require manual changes to the application source code or previous knowledge about its implementation details. The application is monitored at runtime and the anomalies are detected and pinpointed by means of correlation analysis. A recovery procedure is performed every time an anomaly is detected. An experimental study was conducted to evaluate the recovery process included in the SHõWA framework. The experimental environment considers a benchmarking application, installed in a high-availability system. The results show that SHõWA is able to detect and recover from different anomaly scenarios, before any visible error, higher-latency or work-in-progress loss is observed. It proved to be efficient in terms of time of repair. The performance impact induced on the managed system was low: the response time penalty per request varied between 0 and 2.21 milliseconds, the throughput was affected in less than 1%.


autonomic computing; dependability; fail-stutter; performance anomalies; self-healing


IEEE International Symposium on Network Computing and Applications, August 2013


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