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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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| creator | Agarwal, M.K. Gupta, M. Mann, V. Sachindran, N. Anerousis, N. Mummert, L. |
| description | 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 |
| doi_str_mv | 10.1109/NOMS.2006.1687576 |
| format | conference_proceeding |
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| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1542-1201 |
| ispartof | 2006 IEEE/IFIP Network Operations and Management Symposium NOMS 2006, 2006, p.471-482 |
| issn | 1542-1201 2374-9709 |
| language | eng |
| recordid | cdi_ieee_primary_1687576 |
| source | IEEE Xplore All Conference Series |
| subjects | Change Point Detection Degradation Delay Dynamic scheduling Fluctuations Hardware Health Monitoring Manuals Middleware Monitoring Performance loss Problem Determination Storm Drain Storms |
| title | Problem Determination in Enterprise Middleware Systems using Change Point Correlation of Time Series Data |
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