Efficient Migration-Aware Algorithms for Elastic BPMaaS

  • Guillaume RosinoskyEmail author
  • Samir Youcef
  • François Charoy
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10445)


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.


BPM Cloud Elasticity BPM as a service 



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: The source code of the framework is not free for now, except for the segmentation library, available at


  1. 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 2014Google Scholar
  2. 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 2016Google Scholar
  3. 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)CrossRefGoogle Scholar
  4. 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)CrossRefGoogle Scholar
  5. 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 2014Google Scholar
  6. 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 2014Google Scholar
  7. 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 2016Google Scholar
  8. 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_34CrossRefGoogle Scholar
  9. 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 2014Google Scholar
  10. 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)MathSciNetCrossRefGoogle Scholar
  11. 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. 12.
    Kang, J., Park, S.: Algorithms for the variable sized bin packing problem. Eur. J. Oper. Res. 147(2), 365–372 (2003)MathSciNetCrossRefGoogle Scholar
  13. 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. 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. 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 2016Google Scholar
  16. 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: 10.5772/9035CrossRefGoogle Scholar
  17. 17.
    Gurobi Optimization, I.: Gurobi optimizer reference manual (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Guillaume Rosinosky
    • 1
    • 2
    Email author
  • Samir Youcef
    • 2
  • François Charoy
    • 2
  1. 1.BonitasoftGrenobleFrance
  2. 2.Inria Nancy Grand Est - Université de Lorraine - CNRSNancyFrance

Personalised recommendations