Advertisement

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

  • Alireza SouriEmail author
  • Amir Masoud Rahmani
  • Nima Jafari Navimipour
  • Reza Rezaei
Article
  • 14 Downloads

Abstract

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.

Keywords

Service composition Multi-clouds Verification QoS Specification 

Notes

References

  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)Google Scholar
  2. 2.
    Shojafar, M., et al.: Recent advances in cloud data centers toward fog data centers. Concurr. Comput. 31(8), e5164 (2019)CrossRefGoogle 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)CrossRefGoogle Scholar
  5. 5.
    Aceto, G., et al.: Cloud monitoring: a survey. Comput. Netw. 57(9), 2093–2115 (2013)CrossRefGoogle Scholar
  6. 6.
    Stergiou, C., et al.: Secure integration of IoT and cloud computing. Future Gener. Comput. Syst. 78, 964–975 (2018)CrossRefGoogle 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)CrossRefGoogle Scholar
  8. 8.
    Simon, B., Goldschmidt, B., Kondorosi, K.: A metamodel for the web services standards. J. Grid Comput. 11(4), 735–752 (2013)CrossRefGoogle 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)Google Scholar
  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 agentsGoogle Scholar
  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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)MathSciNetCrossRefGoogle 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)CrossRefGoogle Scholar
  18. 18.
    Diekmann, C., et al.: Verified iptables firewall analysis and verification. J. Autom. Reason. 61(1), 191–242 (2018)MathSciNetzbMATHCrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)MathSciNetzbMATHCrossRefGoogle Scholar
  25. 25.
    Yu, B., et al.: Verifying temporal properties of programs: a parallel approach. J. Parallel Distrib. Comput. 118, 89–99 (2018)CrossRefGoogle 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 CrossRefGoogle 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)CrossRefGoogle 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 CrossRefGoogle 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)CrossRefGoogle 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)Google Scholar
  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)Google Scholar
  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)CrossRefGoogle Scholar
  35. 35.
    Saeed, S., et al.: A location-sensitive and network-aware broker for recommending Web services. Computing 101(5), 455–475 (2019)MathSciNetCrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle Scholar
  39. 39.
    Arapinis, M., et al.: Statverif: verification of stateful processes. Journal of Computer Security 22(5), 743–821 (2014)CrossRefGoogle 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)Google Scholar
  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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle Scholar
  46. 46.
    Mardukhi, F., et al.: QoS decomposition for service composition using genetic algorithm. Appl. Soft Comput. 13(7), 3409–3421 (2013)CrossRefGoogle 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)Google Scholar
  48. 48.
    Arunkumar, G., Venkataraman, N.: A novel approach to address interoperability concern in cloud computing. Proc. Comput. Sci. 50, 554–559 (2015)CrossRefGoogle 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)CrossRefGoogle 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

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Engineering, Science and Research BranchIslamic Azad UniversityTehranIran
  2. 2.Department of Computer Engineering, Tabriz BranchIslamic Azad UniversityTabrizIran
  3. 3.Department of Computer Engineering, College of Technical and EngineeringWest Tehran Branch, Islamic Azad UniversityTehranIran

Personalised recommendations