Abstract
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.
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Keywords
- Service Composition
- Composite Service
- Process Instance
- Predictor Manager
- Service Level Agreement Violation
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Leitner, P., Wetzstein, B., Rosenberg, F., Michlmayr, A., Dustdar, S., Leymann, F. (2010). Runtime Prediction of Service Level Agreement Violations for Composite Services. In: Dan, A., Gittler, F., Toumani, F. (eds) Service-Oriented Computing. ICSOC/ServiceWave 2009 Workshops. ServiceWave ICSOC 2009 2009. Lecture Notes in Computer Science, vol 6275. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16132-2_17
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DOI: https://doi.org/10.1007/978-3-642-16132-2_17
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