A hybrid formal verification approach for QoS-aware multi-cloud service composition


Today, cloud providers represent their individual services with several functional and non-functional properties in various environments. Discovering and selecting an appropriate atomic service from a pool of activated services are a main challenge in the multi-cloud service composition. Minimizing the number of cloud providers is a critical matter in the service composition problem, which effects on energy consumption, response time and total cost. This paper presents a hybrid formal verification approach to assess the service composition in multi-cloud environments though the decreasing number of cloud providers to gain final service composition with a high level of Quality of Service (QoS). The presented approach provides behavioral modeling to examine the procedure of user’ requests, service selection, and composition in a multi-cloud environment. Also, the proposed approach permits analysis of the service composition using a Multi-Labeled Transition Systems (MLTS)-based model checking and Pi-Calculus-based process algebra methods for monitoring the functional specifications and non-functional properties as the QoS standards. In addition, the proposed approach satisfies the functional properties for the multi-cloud service composition. The experimental results proved the feasibility of the proposed approach with performance evaluations and some confirmation setups.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11


  1. 1.

    Maamar, Z., et al.: Towards a seamless coordination of cloud and fog: illustration through the internet-of-things. In Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, pp. 2008–2015. ACM, Limassol, Cyprus (2019)

  2. 2.

    Shojafar, M., et al.: Recent advances in cloud data centers toward fog data centers. Concurr. Comput. 31(8), e5164 (2019)

    Google Scholar 

  3. 3.

    Buyya, R., Broberg, J., Goscinski, A.M.: Cloud Computing: Principles and Paradigms, vol. 87. Wiley, Hoboken (2010)

    Google Scholar 

  4. 4.

    Tajiki, M.M., et al.: CECT: computationally efficient congestion-avoidance and traffic engineering in software-defined cloud data centers. Clust. Comput. 21(4), 1881–1897 (2018)

    Google Scholar 

  5. 5.

    Aceto, G., et al.: Cloud monitoring: a survey. Comput. Netw. 57(9), 2093–2115 (2013)

    Google Scholar 

  6. 6.

    Stergiou, C., et al.: Secure integration of IoT and cloud computing. Future Gener. Comput. Syst. 78, 964–975 (2018)

    Google Scholar 

  7. 7.

    Ghobaei-Arani, M., Souri, A.: LP-WSC: a linear programming approach for web service composition in geographically distributed cloud environments. J. Supercomput. 75(5), 2603–2628 (2019)

    Google Scholar 

  8. 8.

    Simon, B., Goldschmidt, B., Kondorosi, K.: A metamodel for the web services standards. J. Grid Comput. 11(4), 735–752 (2013)

    Google Scholar 

  9. 9.

    Piprani, B., Sheppard, D., Barbir, A.: Comparative analysis of SOA and cloud computing architectures using fact based modeling. In: Proceedings of the OTM Confederated International Conferences on the Move to Meaningful Internet Systems. Springer (2013)

  10. 10.

    Portchelvi, V., Venkatesan, V.P., Shanmugasundaram, G.: Achieving web services composition–a survey. Softw. Eng. 2(5), 195–202 (2012)

    Google Scholar 

  11. 11.

    Brahmi, Z., Faten, M.: Service composition in a multi-cloud environment based on cooperative agents

  12. 12.

    Barkat, A., Okba, K., Bourekkache, S.: Service composition in the multi cloud environment. Int. J. Web Inf. Syst. 13(4), 471–484 (2017)

    Google Scholar 

  13. 13.

    Keshanchi, B., Souri, A., Navimipour, N.J.: An improved genetic algorithm for task scheduling in the cloud environments using the priority queues: formal verification, simulation, and statistical testing. J. Syst. Softw. 124, 1–21 (2017)

    Google Scholar 

  14. 14.

    Naseri, A., Navimipour, N.J.: A new agent-based method for QoS-aware cloud service composition using particle swarm optimization algorithm. J. Ambient Intell. Hum. Comput. 10, 1851–1864 (2018)

    Google Scholar 

  15. 15.

    Ghobaei-Arani, M., et al.: CSA-WSC: cuckoo search algorithm for web service composition in cloud environments. Soft Comput. 22, 8353–8378 (2017)

    Google Scholar 

  16. 16.

    Imran, M., et al.: Formal verification and validation of a movement control actor relocation algorithm for safety–critical applications. Wireless Netw. 22(1), 247–265 (2016)

    MathSciNet  Google Scholar 

  17. 17.

    Dumez, C., et al.: Model-driven approach supporting formal verification for web service composition protocols. J. Netw. Comput. Appl. 36(4), 1102–1115 (2013)

    Google Scholar 

  18. 18.

    Diekmann, C., et al.: Verified iptables firewall analysis and verification. J. Autom. Reason. 61(1), 191–242 (2018)

    MathSciNet  MATH  Google Scholar 

  19. 19.

    Ghobaei-Arani, M., et al.: A moth-flame optimization algorithm for web service composition in cloud computing: simulation and verification. Software 48(10), 1865–1892 (2018)

    Google Scholar 

  20. 20.

    Souri, A., Rahmani, A.M., Jafari Navimipour, N.: Formal verification approaches in the web service composition: a comprehensive analysis of the current challenges for future research. Int. J. Commun. Syst. 31(17), 3808 (2018)

    Google Scholar 

  21. 21.

    Souri, A., Navimipour, N.J., Rahmani, A.M.: Formal verification approaches and standards in the cloud computing: a comprehensive and systematic review. Comput. Stand. Interfaces 58, 1–22 (2018)

    Google Scholar 

  22. 22.

    Amato, F., Moscato, F.: Model transformations of MapReduce Design Patterns for automatic development and verification. J. Parallel Distrib. Comput. 110, 52–59 (2017)

    Google Scholar 

  23. 23.

    Souria, A., Shariflooa, M.A., Norouzia, M.: Analyzing SMV & UPPAAL model checkers in real-time systems. Comput. Sci. 1, 631–639 (2012)

    Google Scholar 

  24. 24.

    Frenkel, H., Grumberg, O., Sheinvald, S.: An automata-theoretic approach to model-checking systems and specifications over infinite data domains. J. Autom. eason. 63, 1077–1101 (2018)

    MathSciNet  MATH  Google Scholar 

  25. 25.

    Yu, B., et al.: Verifying temporal properties of programs: a parallel approach. J. Parallel Distrib. Comput. 118, 89–99 (2018)

    Google Scholar 

  26. 26.

    Gao, H., et al.: Research on cost-driven services composition in an uncertain environment. J. Internet Technol.y 20(3), 755–769 (2019)

    Google Scholar 

  27. 27.

    Li, Y., Yao, X.: Cloud manufacturing service composition and formal verification based on extended process calculus. Adv. Mech. Eng. (2018). https://doi.org/10.1177/1687814018781287

    Article  Google Scholar 

  28. 28.

    Bourne, S., Szabo, C., Sheng, Q.Z.: Transactional behavior verification in business process as a service configuration. IEEE Trans. Serv. Comput. 12(2), 290–303 (2019)

    Google Scholar 

  29. 29.

    Souri, A., et al.: Formal modeling and verification of a service composition approach in the social customer relationship management system. Inf. Technol. People (2019). https://doi.org/10.1108/ITP-02-2018-0109

    Article  Google Scholar 

  30. 30.

    Ghobaei-Arani, M., et al.: A moth-flame optimization algorithm for web service composition in cloud computing: simulation and verification. Software 48, 1865–1892 (2018)

    Google Scholar 

  31. 31.

    Mezni, H., Sellami, M.: Multi-cloud service composition using formal concept analysis. J. Syst. Softw. 134, 138–152 (2017)

    Google Scholar 

  32. 32.

    Entezari-Maleki, R., et al.: Modeling and evaluation of service composition in commercial multiclouds using timed colored petri nets. In: IEEE Transactions on Systems, Man, and Cybernetics: Systems. pp. 1–15 (2017)

  33. 33.

    Rai, G.N., et al.: Web service interaction modeling and verification using recursive composition algebra. IEEE Transactions on Services Computing. pp. 1–1 (2018)

  34. 34.

    Khai, H.T., Thang, B.H., Tho, Q.T.: One size does not fit all: logic-based clustering for on-the-fly web service composition and verification. Int. J. Web Grid Serv. 14(3), 237–272 (2018)

    Google Scholar 

  35. 35.

    Saeed, S., et al.: A location-sensitive and network-aware broker for recommending Web services. Computing 101(5), 455–475 (2019)

    MathSciNet  Google Scholar 

  36. 36.

    Wang, H., et al.: Integrating modified cuckoo algorithm and creditability evaluation for QoS-aware service composition. Knowl. Based Syst. 140, 64–81 (2018)

    Google Scholar 

  37. 37.

    Souri, A., et al.: A symbolic model checking approach in formal verification of distributed systems. Human Centric Comput. Inf. Sci. 9(1), 4 (2019)

    Google Scholar 

  38. 38.

    Gyftopoulos, S., Efraimidis, P.S., Katsaros, P.: Formal analysis of DeGroot Influence Problems using probabilistic model checking. Simul. Model. Pract. Theory 89, 144–159 (2018)

    Google Scholar 

  39. 39.

    Arapinis, M., et al.: Statverif: verification of stateful processes. Journal of Computer Security 22(5), 743–821 (2014)

    Google Scholar 

  40. 40.

    Dardha, O., Gay, S.J.: A new linear logic for deadlock-free session-typed processes. In: International Conference on Foundations of Software Science and Computation Structures. Springer (2018)

  41. 41.

    Ryan, M.D., Smyth, B.: Applied pi calculus. J ACM 65, 1 (2011)

    Google Scholar 

  42. 42.

    Rodriguez-Mier, P., et al.: An integrated semantic web service discovery and composition framework. IEEE Trans. Serv. Comput. 9(4), 537–550 (2016)

    Google Scholar 

  43. 43.

    Souri, A., Jafari Navimipour, N.: Behavioral modeling and formal verification of a resource discovery approach in Grid computing. Expert Syst. Appl. 41(8), 3831–3849 (2014)

    Google Scholar 

  44. 44.

    Souri, A., et al.: A model checking approach for user relationship management in the social network. Kybernetes 48(3), 407–423 (2019)

    Google Scholar 

  45. 45.

    Zhao, X., et al.: An improved discrete immune optimization algorithm based on PSO for QoS-driven web service composition. Appl. Soft Comput. 12(8), 2208–2216 (2012)

    Google Scholar 

  46. 46.

    Mardukhi, F., et al.: QoS decomposition for service composition using genetic algorithm. Appl. Soft Comput. 13(7), 3409–3421 (2013)

    Google Scholar 

  47. 47.

    Entezari-Maleki, R., et al.: Modeling and Evaluation of Service Composition in Commercial Multiclouds Using Timed Colored Petri Nets. In: IEEE Transactions on Systems, Man, and Cybernetics: Systems (2017)

  48. 48.

    Arunkumar, G., Venkataraman, N.: A novel approach to address interoperability concern in cloud computing. Proc. Comput. Sci. 50, 554–559 (2015)

    Google Scholar 

  49. 49.

    Rezaei, R., et al.: A semantic interoperability framework for software as a service systems in cloud computing environments. Expert Syst. Appl. 41(13), 5751–5770 (2014)

    Google Scholar 

  50. 50.

    Fatma, L., Haithem, M.: Multicloud service composition: a survey of current approaches and issues. J. Softw. 30(10), e1947 (2018)

    Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Alireza Souri.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Souri, A., Rahmani, A.M., Navimipour, N.J. et al. A hybrid formal verification approach for QoS-aware multi-cloud service composition. Cluster Comput 23, 2453–2470 (2020). https://doi.org/10.1007/s10586-019-03018-9

Download citation


  • Service composition
  • Multi-clouds
  • Verification
  • QoS
  • Specification