Adaptive Monitoring of Web-based Applications: A Performance Study



In this paper we study the performance impact induced by different application-level monitoring tools, targeted for the detection of performance anomalies in Web-based applications. Adaptive and selective algorithms, able to self- adapt the monitoring behavior, are proposed to minimize the performance impact induced by application-level profiling. From the experimental results, becomes clear the usefulness of adaptive and selective monitoring: depending on the system load, the response time latency induced has varied between 0.5 and 14 milliseconds per request; the throughput penalty was inferior to 1%; and the ability to detect and pinpoint the anomalies was not compromised. These outcomes are very favorable to the adoption of application-level profiling and runtime analysis, as a way to detect, pinpoint and repair from anomalies in production systems.


Adaptive Monitoring, Dependability, Performance-faults, Application Profiling, Root-cause analysis


Adaptive Monitoring


28th Symposium On Applied Computing, March 2013

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