|Bibliography||Leitner, Philipp; Wetzstein, Branimir; Rosenberg, Florian; Michlmayr, Anton; Dustdar, Schahram; Leymann, Frank: Runtime Prediction of Service Level Agreement Violations for Composite Services. |
In: Proceedings of the 3rd Workshop on Non-Functional Properties and SLA Management in Service-Oriented Computing, co-located with ICSOC 2009.
University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology.
Springer, November 2009.
Article in Proceedings (Workshop Paper).
|CR-Schema||H.4.1 (Office Automation)|
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.
|Department(s)||University of Stuttgart, Institute of Architecture of Application Systems|
|Entry date||October 21, 2009|