Dynamic Provisioning of Service Composition in a Multi-Tenant SaaS Environment

  • Wael SellamiEmail author
  • Hatem Hadj Kacem
  • Ahmed Hadj Kacem


Multi-tenant service composition has become a common delivery model for business processes in cloud computing. To dynamically support the workload tenant variation, elasticity holds the promise of ensuring the quality of service (QoS) of the business process by providing the involved service instances at a low cost. However, integrating both of multi-tenancy and elasticity during service composition is a key problem for serving multiple tenants from a single process instance. Nowadays, existing approaches in the field of cloud service composition, although numerous, still fall short since they cannot adequately address issues related to supporting the scalability of the composed service and adapting it to the workload fluctuation. In this paper, we propose a holistic approach which makes the dynamic multi-tenant services matching and manages their elasticity in distributed business processes. This approach is based on a generic service pattern that integrates multi-tenancy property and handles elasticity at the process and service levels. Furthermore, we present elastic composition algorithms to compose multi-tenant cloud services and perform their elasticity through the proposed service pattern. The evaluation of our approach, compared to the baseline approach, proves that the latency taken to provide an elastic multi-tenant service composition and detect its SLA (Service Level Agreements) violation are reasonably short. We also show that the CPU overhead of using our approach is negligible. Furthermore, experimental results demonstrate the merits of our approach in terms of minimizing the memory consumption through the deployed service instances.


Cloud service composition Multi-tenancy awareness Elasticity Service patterns 



  1. 1.
    Bohn, R., Messina, J., Liu, F., Tong, J., Mao, J.: NIST cloud computing reference architecture. In: IEEE congress on services (2011), pp. 594–596Google Scholar
  2. 2.
    Chen, H., Zhu, X., Guo, H., Zhu, J., Qin, X., Wu, J.: Towards energy-efficient scheduling for real-time tasks under uncertain cloud computing environment. J. Syst. Softw. 99, 20–35 (2015)CrossRefGoogle Scholar
  3. 3.
    Moens, H., Truyen, E., Walraven, S., Joosen, W., Dhoedt, B., Turck, F.: Cost-effective feature placement of customizable multi-tenant applications in the cloud. J. Netw. Syst. Manag. 22(4), 517–558 (2014)CrossRefGoogle Scholar
  4. 4.
    Mietzner, R., Unger, T., Titze, R., Leymann, F.: Combining different multi-tenancy patterns in service-oriented applications, In: International on enterprise distributed object computing conference (2009), pp. 131–140Google Scholar
  5. 5.
    Simoes, R., Kamienski, C.: Elasticity management in private and hybrid clouds. In: International conference on cloud computing (2014), pp. 793–800Google Scholar
  6. 6.
    Shekhar, S., Abdel-Aziz, H., Bhattacharjee, A., Gokhale, A., Koutsoukos, X.: Performance interference-aware vertical elasticity for cloud-hosted latency-sensitive applications. In: International conference on cloud computing (2018), pp. 82–89Google Scholar
  7. 7.
    Hoenisch, P., Hochreiner, C., Schuller, D., Schulte, S., Mendling, J., Dustdar, S.: Cost-efficient scheduling of elastic processes in hybrid clouds, In: International conference on cloud computing (2015), pp. 17–24Google Scholar
  8. 8.
    Khanam, R., Kumar, R.R., Kumar, C.: QoS based cloud service composition with optimal set of services using PSO, In: International conference on recent advances in information technology (RAIT) (2018), pp. 1–6Google Scholar
  9. 9.
    Huang, J., Li, S., Duan, Q., Yu, R., Yu, S.: QoS Correlation-aware service composition for unified network-cloud service provisioning, In: IEEE global communications conference (GLOBECOM) (2016), pp. 1–6Google Scholar
  10. 10.
    Zhang, M., Liu, L.: Evolutionary algorithm with ahp decision-making method for cloud workflow service composition, In: International conference on cloud computing technology and science (2015), pp. 339–346Google Scholar
  11. 11.
    Amazon Auto Scaling: (2014)
  12. 12.
  13. 13.
    Shen, Z., Subbiah, S., Gu, X., Wilkes, J.: CloudScale: elastic resource scaling for multi-tenant cloud systems, In: Symposium on cloud computing (2011), pp. 1–5Google Scholar
  14. 14.
    Pathirage, M., Perera, S., Kumara, I., Weerasiri, D., Weerawarana, S.: A scalable multi-tenant architecture for business process executions. Int. J. Web Serv. Res. 9(2), 21–41 (2012)CrossRefGoogle Scholar
  15. 15.
    Guo, C., Sun, W., Huang, Y., Hu, W.Z., Gao, B.: A framework for native multi-tenancy application development, management. In: International conference on enterprise computing, E-commerce and E-services (2007), pp. 551–558Google Scholar
  16. 16.
    Krebs, R., Spinner, S., Ahmed, N., Kounev, S.: Resource usage control in multi-tenant applications. In: International symposium on cluster, cloud and grid computing (2014), pp. 122–131Google Scholar
  17. 17.
    Schulte, S., Janiesch, C., Venugopal, S., Weber, I., Hoenisch, P.: Elastic business process management: state of the art, open challenges for BPM in the cloud. Fut. Gener. Comput. Syst. 46, 36–50 (2014)CrossRefGoogle Scholar
  18. 18.
    Suleiman, B., Sakr, S., Jeffery, R., Liu, A.: On understanding the economics and elasticity challenges of deploying business applications on public cloud infrastructure. J. Int. Serv. Appl. 3(2), 173–193 (2012)CrossRefGoogle Scholar
  19. 19.
    Loff, J., Garcia, J.: Vadara: predictive elasticity for cloud applications, In: International conference on cloud computing technology and science (2014), pp. 541–546Google Scholar
  20. 20.
    Sellami, W., Hadj-Kacem, H., Hadj-Kacem, A.: A formal approach for the validation of web service orchestrations. Int. J. Web Portals 5(1), 41–54 (2013)CrossRefGoogle Scholar
  21. 21.
    Noor, T., Sheng, Q., Ngu, A., Dustdar, S.: Analysis of web-scale cloud services. In: IEEE internet computing (2014), pp. 55–61CrossRefGoogle Scholar
  22. 22.
    Sellami, W., Hadj-Kacem, H., Hadj-Kacem, A.: Elastic multi-tenant business process based service pattern in cloud computing. In: International conference on cloud computing technology and science (2014), pp. 154–161Google Scholar
  23. 23.
    Zhi-xue, W., Xin, J., Qing-chao, D., Hong-yue, H., Qing-long, W.: ECA rule modeling language based on UML. In: International conference on computer science, automation engineering (2012), pp. 623–628Google Scholar
  24. 24.
    OpenNebula: (2011)
  25. 25.
    OASIS: Web Services Business Process Execution Language Version 2.0. (2007)
  26. 26.
    WSO2 Business Process Server: (2016)
  27. 27.
    Bray, T., Paoli, J., Sperberg-McQueen, C.M., Maler, E., Yergeau, F.: Extensible Markup Language (XML) 1.0 (Fifth Edition), (2008)
  28. 28.
    Trihinas, G., Truong, D., Moldovan, H.L., Pallis, D., Dustdar, S., Dikaiakos, M.: ADVISE—a framework for evaluating cloud service elasticity behavior. In: International conference on service oriented computing (2014), pp. 275–290Google Scholar
  29. 29.
    Gavvala, S.K., Jatoth, C., Gangadharan, G.R., Buyya, R.: QoS-aware cloud service composition using eagle strategy. Fut. Gener. Comput. Syst. 90, 273–290 (2019)CrossRefGoogle Scholar
  30. 30.
    Amato, F., Moscato, F.: Automatic cloud services composition for big data management. In: International conference on advanced information networking and applications workshops (2016), pp 46–51Google Scholar
  31. 31.
    Lui, S., Wei, Y., Tang, K., Qin, A., Yao, X.: Qos-aware long-term based service composition in cloud computing. In: Congress on evolutionary computation (2015), pp. 382–393Google Scholar
  32. 32.
    Cai, H., Cui, L., Shi, Y., Kong, L., Yan, Z.: Multi-tenant service composition based on granularity computing. In: International conference on services computing (2014), pp. 669–676Google Scholar
  33. 33.
    Liu, J., Qiao, J., Zhao, J.: FEMCRA: Fine-grained elasticity measurement for cloud resources allocation. In: International conference on cloud computing (2018), pp. 732–739Google Scholar
  34. 34.
    Jrad, A.B., Bhiri, S., Tata, S.: Data-aware modeling of elastic processes for elasticity strategies evaluation. In: International conference on cloud computing (2017), pp. 464–471Google Scholar
  35. 35.
    Jrada, A., Bhiria, S., Tata, S.: Description and evaluation of elasticity strategies for business processes in the cloud. In: International conference on services computing (2016), pp. 203–210Google Scholar
  36. 36.
    Boubaker, S., Mammar, A., Graiet, M., Gaaloul, W.: Formal verification of cloud resource allocation in business processes using event-B. In: International conference on advanced information networking and applications (2016), pp. 746–753Google Scholar
  37. 37.
    Hoenisch, P., Schulte, S., Dustdar, S., Venugopal, S.: Self-adaptive resource allocation for elastic process execution, In: International conference on cloud computing (2013), pp. 220–227Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  1. 1.ReDCAD LaboratoryUniversity of SfaxSfaxTunisia

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