Efficient Migration-Aware Algorithms for Elastic BPMaaS

  • Guillaume Rosinosky
  • Samir Youcef
  • François Charoy
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

BPM Cloud Elasticity BPM as a service 

Notes

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/.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

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

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