Detecção de anomalias em aplicações Web utilizando filtros baseados em coeficiente de correlação parcial
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Data
2014-10-31
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Universidade Federal de Goiás
Resumo
Finding faults or causes of performance problems in modernWeb computer systems is an
arduous task that involves many hours of system metrics monitoring and log analysis. In
order to aid administrators in this task, many anomaly detection mechanisms have been
proposed to analyze the behavior of the system by collecting a large volume of statistical
information showing the condition and performance of the computer system. One of the
approaches adopted by these mechanism is the monitoring through strong correlations
found in the system. In this approach, the collection of large amounts of data generate
drawbacks associated with communication, storage and specially with the processing
of information collected. Nevertheless, few mechanisms for detecting anomalies have a
strategy for the selection of statistical information to be collected, i.e., for the selection
of monitored metrics. This paper presents three metrics selection filters for mechanisms
of anomaly detection based on monitoring of correlations. These filters were based on
the concept of partial correlation technique which is capable of providing information
not observable by common correlations methods. The validation of these filters was
performed on a scenario of Web application, and, to simulate this environment, we use
the TPC-W, a Web transactions Benchmark of type E-commerce. The results from our
evaluation shows that one of our filters allowed the construction of a monitoring network
with 8% fewer metrics that state-of-the-art filters, and achieve fault coverage up to 10%
more efficient.
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Citação
SILVA, O. J. A. P. da. Detecção de anomalias em aplicações Web utilizando filtros baseados em coeficiente de correlação parcial. 2014. 81 f. Dissertação (Mestrado em Ciência da Computação) - Universidade Federal de Goiás, Goiânia, 2014.