Runtime Prediction

Conference paper


Monitoring the preservation of quality of service (QoS) properties during the operation of service-based systems at runtime is an important verification measure for determining whether current service usage is compliant with agreed SLAs. Monitoring, however, does not always provide sufficient scope for taking control actions against violations, as it only detects violations after they occur. This chapter describes a model-based prediction framework for detecting potential violations of QoS properties before they occur to enable the undertaking of control actions that could prevent the violations. EVEREST+ receives prediction specifications expressed in Event Calculus and automatically identifies relevant monitoring data that should be collected at runtime to infer QoS property prediction models. It then analyses runtime monitoring data to infer statistical prediction models for the relevant properties, and uses the models to detect potential violations of QoS properties and the probability of such violations.


Service Level Agreement Service Level Agreement Violation Event Calculus Prediction Framework Agreement Term 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Duc, B.L., Chˆatel, P., Rivierre, N., Malenfant, J., Collet, P., Truck, I.: Nonfunctional data collection for adaptive business processes and decision making. In: Proceedings of the 4th International Workshop on Middleware for Service Oriented Computing, MWSOC ’09, pp. 7–12. ACM, New York, NY, USA (2009)Google Scholar
  2. 2.
    Kearney, K., Torelli, F., Kotsokalis, C.: SLA*: An abstract syntax for service level agreements (2010). Developed by the the FP7 EU project SLA@SOI. To be publishedGoogle Scholar
  3. 3.
    Kowalski, R., Sergot, M.: A logic-based calculus of events. New Gen. Comput. 4(1), 67–95 (1986)CrossRefGoogle Scholar
  4. 4.
    L’Ecuyer, P., Meliani, L., Vaucher, J.: Ssj: a framework for stochastic simulation in java. Winter Simulation Conference 1, 234–242 (2002)Google Scholar
  5. 5.
    Leitner, P.,Wetzstein, B., Rosenberg, F., Michlmayr, A., Dustdar, S., Leymann, F.: Runtime prediction of service level agreement violations for composite services. In: A. Dan, F. Gittler, F. Toumani (eds.) Service-Oriented Computing – Revised Selected Papers of ICSOC/ServiceWave 2009 Workshops, tockholm, Sweden, November 23–27, 2009, Lecture Notes in Computer Science, vol. 6275, pp. 176–186. Springer, Berlin / Heidelberg (2010)Google Scholar
  6. 6.
    Lorenzoli, D., Spanoudakis, G.: EVEREST+: run-time sla violations prediction. In: Proceedings of the 5th International Workshop on Middleware for Service Oriented Computing, MW4SOC, pp. 13–18. ACM, New York, NY,USA (2010)Google Scholar
  7. 7.
    Mahbub, K., Spanoudakis, G.: Monitoring ws-agreements: An event calculus based approach. In: In Test and Analysis of Web Services, (eds) Baresi L. & di Nitto E, pp. 265–306. Springer Verlang (2007)Google Scholar
  8. 8.
    Michlmayr, A., Rosenberg, F., Leitner, P., Dustdar, S.: Comprehensive qos monitoring of web services and event-based sla violation detection. In: Proceedings of the 4th International Workshop on Middleware for Service Oriented Computing, MWSOC ’09, pp. 1–6. ACM, New York, NY, USA (2009)Google Scholar
  9. 9.
    Michlmayr, A., Rosenberg, F., Leitner, P., Dustdar, S.: End-to-end support for qos-aware service selection, binding, and mediation in vresco. IEEE Transactions on Services Computing 3, 193–205 (2010)CrossRefGoogle Scholar
  10. 10.
    Salfner, F., Schieschke, M., Malek, M.: Predicting failures of computer systems: a case study for a telecommunication system. In: Parallel and Distributed Processing Symposium, 2006. IPDPS 2006. 20th International, p. 8 (2006)Google Scholar
  11. 11.
    Theocharis Tsigkritis George Spanoudakis, C.K., Lorenzoli, D.: Diagnosis and Threat Detection Capabilities of the SERENITY Monitoring Framework, Advances in Information Security, vol. 45, chap. 14, pp. 239–271. Springer US (2009)Google Scholar
  12. 12.
    Thio, N., Karunasekera, S.: Automatic measurement of a qos metric for web service recommendation. Software Engineering Conference, Australian 0, 202–211 (2005)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Department of ComputingCity University LondonLondonUK

Personalised recommendations