Abstract
Service-based processes represent compositions of software services that need to be properly executed by the resources offered by an IT infrastructure within a company. Due to the dynamic changes in their QoS requirements, service-based processes are constantly evolving and demanding new resources. To ensure agility and support more flexibility, it is common today for enterprises to outsource their service-based processes to cloud environments and recently to cloud federations. The main challenge in this regard is to ensure an optimal allocation of cloud resources to process services overtime. In fact, given the diversity of the resources within a federation and the continuous changes of the process QoS needs, the reallocation of cloud resources to process services may result in high computing costs and an increase in the communication overheads. In this paper, we propose a novel adaptive resource allocation approach which can estimate and optimize the final deployment costs. We use agent-based systems to simulate processes’ enactment. To cope with the services’ QoS changes and dynamically adapt the initial deployment, we propose an extended version of the Pairwise-Movement Fiduccia-Mattheyses (E-PMFM) partitioning algorithm. Our experimental results highlight the efficiency of E-PMFM algorithm and show that deployment costs are sensitive to the initial deployment and the used partitioning algorithm.
This is a preview of subscription content, log in via an institution to check access.
Notes
- 1.
An agent is equivalent to a service within the process. In the remainder, we will use the term agent instead of service.
- 2.
Note that we also include t0 in \(\xi \) in order to be able to compute the initial deployment.
References
Cong, J., Lim, S.K.: Multiway partitioning with pairwise movement. In: Proceedings of the 1998 IEEE/ACM International Conference on Computer-aided Design, ICCAD 1998, pp. 512–516, ACM, New York, NY, USA (1998)
Endert, H., Küster, T., Hirsch, B., Albayrak, S.: Mapping BPMN to agents: an analysis. Agents, Web-Services, and Ontologies Integrated Methodologies, pp. 43–58 (2007)
Ferber, J.: Multi-agent Systems: An Introduction to Distributed Artificial Intelligence, vol. 1. Addison-Wesley, Reading (1999)
Fiduccia, C.M., Mattheyses, R.M.: A linear-time heuristic for improving network partitions. In: 19th Design Automation Conference, pp. 175–181, June 1982
Hoenisch, P., Hochreiner, C., Schuller, D., Schulte, S., Mendling, J., Dustdar, S.: Cost-efficient scheduling of elastic processes in hybrid clouds. In: 2015 IEEE 8th International Conference on Cloud Computing, pp. 17–24, June 2015
Hoenisch, P., Schulte, S., Dustdar, S., Venugopal, S.: Self-adaptive resource allocation for elastic process execution. In: 2013 IEEE Sixth International Conference on Cloud Computing, pp. 220–227, June 2013
Küster, T., Heßler, A., Albayrak, S.: Towards process-oriented modelling and creation of multi-agent systems. In: Dalpiaz, F., Dix, J., van Riemsdijk, M.B. (eds.) EMAS 2014. LNCS, vol. 8758, pp. 163–180. Springer, Cham (2014). doi:10.1007/978-3-319-14484-9_9
Mastelic, T., Fdhila, W., Brandic, I., Rinderle-Ma, S.: Predicting resource allocation and costs for business processes in the cloud. In: 2015 IEEE World Congress on Services, pp. 47–54, June 2015
Odell, J., Nodine, M., Levy, R.: A metamodel for agents, roles, and groups. In: Odell, J., Giorgini, P., Müller, J.P. (eds.) AOSE 2004. LNCS, vol. 3382, pp. 78–92. Springer, Heidelberg (2005). doi:10.1007/978-3-540-30578-1_6
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, June 2016
Verbelen, T., Stevens, T., Turck, F.D., Dhoedt, B.: Graph partitioning algorithms for optimizing software deployment in mobile cloud computing. Future Gener. Comput. Syst. 29(2), 451–459 (2013). special section: Recent advances in e-Science
Wooldridge, M., Jennings, N.R., Kinny, D.: The Gaia methodology for agent-oriented analysis and design. Auton. Agent. Multi-Agent Syst. 3(3), 285–312 (2000)
Yangui, S., Klai, K., Tata, S.: Deployment of service-based processes in the cloud using petri net decomposition. In: Meersman, R., Panetto, H., Dillon, T., Missikoff, M., Liu, L., Pastor, O., Cuzzocrea, A., Sellis, T. (eds.) OTM 2014. LNCS, vol. 8841, pp. 57–74. Springer, Heidelberg (2014). doi:10.1007/978-3-662-45563-0_4
Acknowledgment
This work was financially supported by the PHC Utique program of the French Ministry of Foreign Affairs and Ministry of higher education and research and the Tunisian Ministry of higher education and scientific research in the CMCU project number 15G1413.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Labba, C., Assy, N., Saoud, N.B.B., Gaaloul, W. (2017). Adaptive Deployment of Service-Based Processes into Cloud Federations. In: Bouguettaya, A., et al. Web Information Systems Engineering – WISE 2017. WISE 2017. Lecture Notes in Computer Science(), vol 10569. Springer, Cham. https://doi.org/10.1007/978-3-319-68783-4_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-68783-4_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-68782-7
Online ISBN: 978-3-319-68783-4
eBook Packages: Computer ScienceComputer Science (R0)