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Data-driven and automated prediction of service level agreement violations in service compositions


Service Level Agreements (SLAs), i.e., contractually binding agreements between service providers and clients, are gaining momentum as the main discriminating factor between service implementations. For providers, SLA compliance is of utmost importance, as violations typically lead to penalty payments or reduced customer satisfaction. In this paper, we discuss approaches to predict violations a priori. This allows operators to take timely remedial actions, and prevent SLA violations before they have occurred. We discuss data-driven, statistical approaches for both, instance-level prediction (SLA compliance prediction for an ongoing business process instance) and forecasting (compliance prediction for future instances). We present an integrated framework, and numerically evaluate our approach based on a case study from the manufacturing domain.

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The research leading to these results has received funding from the European Community’s Seventh Framework Program [FP7/2007-2013] under grant agreement 257483 (Indenica), as well as from the Austrian Science Fund (FWF) under project references P23313-N23 (Audit 4 SOAs).

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Correspondence to Philipp Leitner.

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Communicated by Amit Sheth.

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Leitner, P., Ferner, J., Hummer, W. et al. Data-driven and automated prediction of service level agreement violations in service compositions. Distrib Parallel Databases 31, 447–470 (2013). https://doi.org/10.1007/s10619-013-7125-7

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  • Service composition
  • Service level agreements
  • Quality prediction