Runtime prediction of service level agreement violations for composite services

  • Authors:

  • Philipp Leitner;Branimir Wetzstein;Florian Rosenberg;Anton Michlmayr;Schahram Dustdar;Frank Leymann

  • Affiliations:

  • Vienna University of Technology, Vienna, Austria;Institute of Architecture of Application Systems, University of Stuttgart, Stuttgart, Germany;CSIRO ICT Centre, Canberra, ACT, Australia;Vienna University of Technology, Vienna, Austria;Vienna University of Technology, Vienna, Austria;Institute of Architecture of Application Systems, University of Stuttgart, Stuttgart, Germany

  • Venue:
  • ICSOC/ServiceWave'09 Proceedings of the 2009 international conference on Service-oriented computing
  • Year:
  • 2009

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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.