CEP-Based SLO Evaluation

  • Kyriakos Kritikos
  • Chrysostomos Zeginis
  • Andreas Paravoliasis
  • Dimitris Plexousakis
Conference paper
First Online:
Part of the Communications in Computer and Information Science book series (CCIS, volume 824)

Abstract

Modern service-based applications (SBAs) operate in highly dynamic environments where both underlying resources and the application demand can be constantly changing which external SBA components might fail. Thus, they need to be rapidly modified to address such changes. Such a rapid updating should be performed across multiple levels to better deal, in an orchestrated and globally-consistent manner, with the current problematic situation. First of all, this means that a fast and scalable event generation and detection mechanism should exist to rapidly trigger the adaptation workflow to be performed. Such a mechanism needs to handle all kinds of events occurring at different abstraction levels and to compose them so as to detect more advanced situations. To this end, this paper introduces a new complex event processing framework able to realise the respective features mentioned (processing speed, scalability) and have the flexibility to capture and sense any kind of event or event combination occurring in the SBA system. Such a framework is wrapped in the form of a REST service enabling to manage the event patterns that need to be rapidly detected. It is also well connected to other main components of the SBA management system, via a publish-subscribe mechanism, including monitoring and the adaptation engines.

Keywords

Complex event processing Event pattern Detection Service 
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Notes

Acknowledgments

This work is supported by CloudSocket project that has been funded within the European Commission’s H2020 Program under contract number 644690.

References

  1. 1.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: VLDB, pp. 487–499 (1994)Google Scholar
  2. 2.
    Artikis, A., Sergot, M.J., Paliouras, G.: Run-time composite event recognition. In: DEBS, pp. 69–80. ACM (2012)Google Scholar
  3. 3.
    Bettini, C., Wang, X.S., Jajodia, S., Lin, J.-L.: Discovering frequent event patterns with multiple granularities in time sequences. IEEE Trans. Knowl. Data Eng. 10(2), 222–237 (1998)CrossRefGoogle Scholar
  4. 4.
    Blair, G., Bencomo, N., France, R.B.: Models@ run.time. Computer 42(10), 22–27 (2009)CrossRefGoogle Scholar
  5. 5.
    Seybold, D., Griesinger, F., Kritikos, K., Gallo, A., Cacciatore, S., Popovici, A., Iranzo, J., Sosa, R., Utz, W., Falcioni, D.: Explanatory Notes: Final BPaaS Prototype. CloudSocket Project Deliverable D4.6–D4.8, June 2017Google Scholar
  6. 6.
    Euting, S., Janiesch, C., Fischer, R., Tai, S., Weber, I.: Scalable business process execution in the cloud. In: 2nd IEEE Conference on Cloud Engineering (IC2E), pp. 175–184. IEEE (2014)Google Scholar
  7. 7.
    Ferry, N., Chauvel, F., Rossini, A., Morin, B., Solberg, A.: Managing multi-cloud systems with CloudMF. In: NordiCloud, pp. 38–45. ACM (2013)Google Scholar
  8. 8.
    Ghosh, R., Ghose, A., Hegde, A., Mukherjee, T., Mos, A.: QoS-driven management of business process variants in cloud based execution environments. In: Sheng, Q.Z., Stroulia, E., Tata, S., Bhiri, S. (eds.) ICSOC 2016. LNCS, vol. 9936, pp. 55–69. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46295-0_4CrossRefGoogle Scholar
  9. 9.
    Hellerstein, J.L., Ma, S., Perng, C.-S.: Discovering actionable patterns in event data. IBM Syst. J. 41(3), 475–493 (2002)CrossRefGoogle Scholar
  10. 10.
    Janiesch, C., Weber, I., Menzel, M., Kuhlenkamp, J.: Optimizing the performance of automated business processes executed on virtualized infrastructure. In: 47th Hawaii International Conference on System Sciences (HICSS), pp. 3818–3826. IEEE (2014)Google Scholar
  11. 11.
    Kazhamiakin, R., Pistore, M., Zengin, A.: Cross-layer adaptation and monitoring of service-based applications. In: Dan, A., Gittler, F., Toumani, F. (eds.) ICSOC/ServiceWave - 2009. LNCS, vol. 6275, pp. 325–334. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-16132-2_31CrossRefGoogle Scholar
  12. 12.
    Kritikos, K., Domaschka, J., Rossini, A.: SRL: a scalability rule language for multi-cloud environments. In: CloudCom. IEEE (2014)Google Scholar
  13. 13.
    Magnusson, M.S.: Discovering hidden time patterns in behavior: T-patterns and their detection. Behav. Res. Methods Instr. Comput. 32(1), 93–110 (2000)CrossRefGoogle Scholar
  14. 14.
    Manku, G.S., Motwani, R.: Approximate frequency counts over data streams, pp. 346–357 (2002)Google Scholar
  15. 15.
    Patnaik, D., Ramakrishnan, N., Laxman, S., Chandramouli, B.: Streaming algorithms for pattern discovery over dynamically changing event sequences. CoRR, abs/1205.4477 (2012)Google Scholar
  16. 16.
    Römer, K.: Distributed mining of spatio-temporal event patterns in sensor networks. In: EAWMS Workshop at DCOSS, pp. 103–116 (2006)Google Scholar
  17. 17.
    Sim, A.T.H., Indrawan, M., Zutshi, S., Srinivasan, B.: Logic-based pattern discovery. IEEE Trans. Knowl. Data Eng. 22(6), 798–811 (2010)CrossRefGoogle Scholar
  18. 18.
    Wang, D., Rundensteiner, E.A., Ellison, R.T.: Active complex event processing over event streams. PVLDB 4(10), 634–645 (2011)Google Scholar
  19. 19.
    Wu, E., Diao, Y., Rizvi, S.: High-performance complex event processing over streams. In: SIGMOD Conference, pp. 407–418. ACM (2006)Google Scholar
  20. 20.
    Zeginis, C., Kritikos, K., Plexousakis, D.: Event pattern discovery for cross-layer adaptation of multi-cloud applications. In: Villari, M., Zimmermann, W., Lau, K.-K. (eds.) ESOCC 2014. LNCS, vol. 8745, pp. 138–147. Springer, Heidelberg (2014).  https://doi.org/10.1007/978-3-662-44879-3_10CrossRefGoogle Scholar
  21. 21.
    Zeginis, C., Kritikos, K., Plexousakis, D.: Event pattern discovery in multi-cloud service-based applications. IJSSOE 5(4), 78–103 (2015)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute of Computer Science - FORTHHeraklionGreece
  2. 2.Computer Science DepartmentUniversity of CreteHeraklionGreece

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