Problem Determination in Enterprise Middleware Systems using Change Point Correlation of Time Series Data

Clustered enterprise middleware systems employing dynamic workload scheduling are susceptible to a variety of application malfunctions that can manifest themselves in a counterintuitive fashion and cause debilitating damage. Until now, diagnosing problems in that domain involves investigating log fi...

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Bibliographic Details
Main Authors: Agarwal, M.K., Gupta, M., Mann, V., Sachindran, N., Anerousis, N., Mummert, L.
Format: Conference Proceeding
Language:English
Subjects:
Change Point Detection
Degradation
Delay
Dynamic scheduling
Fluctuations
Hardware
Health Monitoring
Manuals
Middleware
Monitoring
Performance loss
Problem Determination
Storm Drain
Storms
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Summary:Clustered enterprise middleware systems employing dynamic workload scheduling are susceptible to a variety of application malfunctions that can manifest themselves in a counterintuitive fashion and cause debilitating damage. Until now, diagnosing problems in that domain involves investigating log files and configuration settings and requires in-depth knowledge of the middleware architecture and application design. This paper presents a method for problem determination using change point detection techniques and problem signatures consisting of a combination of changes (or absence of changes) in different metrics. We implemented this approach on a clustered middleware system and applied it to the detection of the storm drain condition: a debilitating problem encountered in clustered systems with counterintuitive symptoms. Our experimental results show that the system detects 93% of storm drain faults with no false positives
ISSN:1542-1201
2374-9709
DOI:10.1109/NOMS.2006.1687576