[Back]

Ph. Leitner, B. Wetzstein, F. Rosenberg, A. Michlmayr, S. Dustdar, F. Leymann:
"Runtime Prediction of Service Level Agreement Violations for Composite Services";
Talk: 3rd Workshop on Non-Functional Properties and SLA Management in Service-Oriented Computing, co-located with ICSOC 2009, Stockholm, Sweden; 11-23-2009 - 11-27-2009; in: "Service-Oriented Computing ICSOC/ServiceWave 2009 Workshops Revised Selected Papers", A. Dan, F. Gittler, F. Toumani (ed.); Springer, LNCS 6275 (2010), ISBN: 978-3-642-16131-5; 176 - 186.

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 basics of our approach, and briefly validate our ideas based
on an illustrative example.

Project Head Schahram Dustdar:
S-Cube