  The IT industry needs systems management models that leverage available
application information to detect quality of service, scalability and health of
service. Ideally this technique would be common for varying application types
with different n-tier architectures under normal production conditions of
varying load, user session traffic, transaction type, transaction mix, and
hosting environment.
  This paper shows that a whole of service measurement paradigm utilizing a
black box M/M/1 queuing model and auto regression curve fitting of the
associated CDF are an accurate model to characterize system performance
signatures. This modeling method is also used to detect application slow down
events. The technique was shown to work for a diverse range of workloads
ranging from 76 Tx/ 5min to 19,025 Tx/ 5min. The method did not rely on
customizations specific to the n-tier architecture of the systems being
analyzed and so the performance anomaly detection technique was shown to be
platform and configuration agnostic.
