Skip to main content
SpringerLink
Log in
Book cover

International Conference on Business Process Management

BPM 2017: Business Process Management pp 147–163Cite as

Efficient Migration-Aware Algorithms for Elastic BPMaaS

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10445))

Abstract

As for all kind of software, customers expect to find business process execution provided as a service (BPMaaS). They expect it to be provided at the best cost with guaranteed SLA. From the BPMaaS provider point of view it can be done thanks to the provision of an elastic cloud infrastructure. Providers still have to provide the service at the lowest possible cost while meeting customers expectation. We propose a customer-centric service model that link the BP execution requirement to cloud resources, and that optimize the deployment of customer’s (or tenants) processes in the cloud to adjust constantly the provision to the needs. However, migrations between cloud configurations can be costly in terms of quality of service and a provider should reduce the number of migrations. We propose a model for BPMaaS cost optimization that take into account a maximum number of migrations for each tenants. We designed a heuristic algorithm and experimented using various customer load configurations based on customer data, and on an actual estimation of the capacity of cloud resources.

This is a preview of subscription content, log in via an institution.

Notes

  1. 1.

    http://www.bonitasoft.com/.

  2. 2.

    https://github.com/alchemyst/Segmentation developed by Carl Sandrock for his paper [16].

References

  1. Le, T.M.H., Alfredo, L.A., Choi, H.R., Cho, M.J., Kim, C.S.: A study on BPaaS with TCO model, pp. 249–256. IEEE, December 2014

    Google Scholar 

  2. Rosinosky, G., Youcef, S., Charoy, F.: An efficient approach for multi-tenant elastic business processes management in cloud computing environment. In: 2016 IEEE 9th International Conference on Cloud Computing (CLOUD), pp. 311–318. IEEE, June 2016

    Google Scholar 

  3. Schulte, S., Janiesch, C., Venugopal, S., Weber, I., Hoenisch, P.: Elastic business process management: state of the art and open challenges for BPM in the cloud. Future Gener. Comput. Syst. 46, 36–50 (2014)

    Article  Google Scholar 

  4. Hoenisch, P., Schuller, D., Schulte, S., Hochreiner, C., Dustdar, S.: Optimization of complex elastic processes. IEEE Trans. Services Comput. 9(5), 700–713 (2016)

    Article  Google Scholar 

  5. Janiesch, C., Weber, I., Kuhlenkamp, J., Menzel, M.: Optimizing the performance of automated business processes executed on virtualized infrastructure. In: 2014 47th Hawaii International Conference on System Sciences (HICSS), pp. 3818–3826. IEEE, January 2014

    Google Scholar 

  6. Euting, S., Janiesch, C., Fischer, R., Tai, S., Weber, I.: Scalable business process execution in the cloud. In: 2014 IEEE International Conference on Cloud Engineering (IC2E), pp. 175–184, March 2014

    Google Scholar 

  7. Rekik, M., Boukadi, K., Assy, N., Gaaloul, W., Ben-Abdallah, H.: A linear program for optimal configurable business processes deployment into cloud federation. In: 2016 IEEE International Conference on Services Computing (SCC), pp. 34–41. IEEE, June 2016

    Google Scholar 

  8. Hachicha, E., Assy, N., Gaaloul, W., Mendling, J.: A configurable resource allocation for multi-tenant process development in the cloud. In: Nurcan, S., Soffer, P., Bajec, M., Eder, J. (eds.) CAiSE 2016. LNCS, vol. 9694, pp. 558–574. Springer, Cham (2016). doi:10.1007/978-3-319-39696-5_34

    Chapter  Google Scholar 

  9. Sellami, W., Kacem, H.H., Kacem, A.H.: Elastic multi-tenant business process based service pattern in cloud computing. In: 2014 IEEE 6th International Conference on Cloud Computing Technology and Science (CloudCom), pp. 154–161. IEEE, December 2014

    Google Scholar 

  10. Das, S., Agrawal, D., El Abbadi, A.: ElasTraS: an elastic, scalable, and self-managing transactional database for the cloud. ACM Trans. Database Syst. 38(1), 1–45 (2013)

    Article  MathSciNet  Google Scholar 

  11. Barker, S.K., Chi, Y., Hacigümüs, H., Shenoy, P.J., Cecchet, E.: ShuttleDB: database-aware elasticity in the cloud. In: 11th International Conference on Autonomic Computing, ICAC 2014, Philadelphia, PA, USA, 18–20 June 2014, pp. 33–43 (2014)

    Google Scholar 

  12. Kang, J., Park, S.: Algorithms for the variable sized bin packing problem. Eur. J. Oper. Res. 147(2), 365–372 (2003)

    Article  MathSciNet  Google Scholar 

  13. Lovrić, M., Milanović, M., Stamenković, M.: Algoritmic methods for segmentation of time series: an overview. J. Contemp. Econ. Bus. Issues 1(1), 31–53 (2014)

    Google Scholar 

  14. Keogh, E., Chu, S., Hart, D., Pazzani, M.: An online algorithm for segmenting time series. In: 2001, Proceedings IEEE International Conference on Data Mining, ICDM, pp. 289–296. IEEE (2001)

    Google Scholar 

  15. Rosinosky, G., Youcef, S., Charoy, F.: A framework for BPMS performance and cost evaluation on the cloud. In: 2016 IEEE International Conference on Cloud Computing Technology and Science (CloudCom), pp. 653–658. IEEE, December 2016

    Google Scholar 

  16. Sandrock, C.: Identification and generation of realistic input sequences for stochastic simulation with Markov processes. In: Cakaj, S. (ed.) Modeling Simulation and Optimization - Tolerance and Optimal Control. InTech, April 2010. doi:10.5772/9035

    Chapter  Google Scholar 

  17. Gurobi Optimization, I.: Gurobi optimizer reference manual (2015)

    Google Scholar 

Download references

Acknowledgements

The authors would like to thank Gurobi for the usage of their optimizer, and Amazon Web Services for the EC2 instances credits (this paper is supported by an AWS in Education Research Grant Award). The data and the results are available at: http://doi.org/10.5281/zenodo.401374. The source code of the framework is not free for now, except for the segmentation library, available at https://github.com/guillaumerosinosky/Segmentation/.

Author information

Authors and Affiliations

  1. Bonitasoft, Grenoble, France

    Guillaume Rosinosky

  2. Inria Nancy Grand Est - Université de Lorraine - CNRS, Nancy, France

    Guillaume Rosinosky, Samir Youcef & François Charoy

Authors
  1. Guillaume Rosinosky
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Samir Youcef
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. François Charoy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Guillaume Rosinosky .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Universitat Politècnica de Catalunya, Barcelona, Spain

    Josep Carmona

  2. Department of Computer Science, University of Paderborn, Paderborn, Germany

    Gregor Engels

  3. Department of Supply Chain and Information Systems, Pennsylvania State University, University Park, Pennsylvania, USA

    Akhil Kumar

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Rosinosky, G., Youcef, S., Charoy, F. (2017). Efficient Migration-Aware Algorithms for Elastic BPMaaS. In: Carmona, J., Engels, G., Kumar, A. (eds) Business Process Management. BPM 2017. Lecture Notes in Computer Science(), vol 10445. Springer, Cham. https://doi.org/10.1007/978-3-319-65000-5_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-65000-5_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64999-3

  • Online ISBN: 978-3-319-65000-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation