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The IT industry needs systems management models that leverage availableapplication information to detect quality of service, scalability and health ofservice. Ideally this technique would be common for varying application typeswith different n-tier architectures under normal production conditions ofvarying load, user session traffic, transaction type, transaction mix, andhosting environment.  This paper shows that a whole of service measurement paradigm utilizing ablack box M/M/1 queuing model and auto regression curve fitting of theassociated CDF are an accurate model to characterize system performancesignatures. This modeling method is also used to detect application slow downevents. The technique was shown to work for a diverse range of workloadsranging from 76 Tx/ 5min to 19,025 Tx/ 5min. The method did not rely oncustomizations specific to the n-tier architecture of the systems beinganalyzed and so the performance anomaly detection technique was shown to beplatform and configuration agnostic.

"The tail wags the dog": A study of anomaly detection in commercial application performance