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Ph. Leitner, B. Wetzstein, F. Rosenberg, A. Michlmayr, S. Dustdar, F. Leymann:
"Runtime Prediction of Service Level Agreement Violations for Composite Services";
Report No. TUV-1841-2010-02, 2010; 16 pages.

SLAs are contractually binding agreements between service
providers and consumers, mandating concrete numerical target values
which the service needs to achieve. For service providers, it is essential
to prevent SLA violations as much as possible to enhance customer
satisfaction and avoid penalty payments. Therefore, it is desirable for
providers to predict possible violations before they happen, while it is
still possible to set counteractive measures. We propose an approach
for predicting SLA violations at runtime, which uses measured and estimated
facts (instance data of the composition or QoS of used services)
as input for a prediction model. The prediction model is based on machine
learning regression techniques, and trained using historical process
instances. We present the architecture of our approach and a prototype
implementation, and validate our ideas based on an illustrative example.

Service-oriented Computing,Web Services, SLA, Prediction of SLA Violations