Classification of SOA-Based Cloud Services Using Data Mining Technique

  • Zeenat ParweenEmail author
  • R. B. S. Yadav
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1154)


Cloud computing model fulfills storage and computation requirements of users in the form of services that are based on Service-Oriented Architecture (SOA). Services in cloud computing are offered with different quality attributes such as flexibility, mobility, adaptation, migration, etc. In recent years, cloud computing has grown at a very fast pace offering a huge number of cloud services with various quality attributes but it still faces some issues. One of them is that there is a lack of approach which can classify a cloud service based on its quality attributes for the cloud users. In this paper, a new approach is presented to classify cloud services using data mining “Bayesian classification” technique. The proposed approach is evaluated empirically via experiments that are performed on cloud-based data set containing data for 20 services, where each service having ten quality attributes. Results show that the proposed approach successfully classifies the cloud service in one of three classes (best, good, and satisfactory). Further, the proposed research will help the users in selecting the services as per their desired class.


Service-oriented architecture Cloud Data mining Classification Quality 


  1. 1.
    Erl, T.: Service-Oriented Architecture: Concepts, Technology, and Design. Pearson Education India (2005)Google Scholar
  2. 2.
    Zhang, L.-J.: SOA and Web services. In: IEEE International Conference on Services Computing, 2006, SCC’06, pp. xxxvi–xxxvi (2006)Google Scholar
  3. 3.
    Laskey, K.B., Laskey, K.: Service oriented architecture. Wiley Interdiscip. Rev. Comput. Stat. 1(1), 101–105 (2009)CrossRefGoogle Scholar
  4. 4.
    Vaquero, L.M., Rodero-Merino, L., Morn, D.: Locking the sky: a survey on IaaS cloud security. J. Comput. 91(1), 93,1/2011 1-26A (2011) (Springer Verlag)Google Scholar
  5. 5.
    Keith, J., Burkhard, N.: The future of cloud computing. Opportunities for European Cloud Computing Beyond. Expert group report. (2010)
  6. 6.
    Takako, P., Estcio, G., Kelner, J., Sadok, D.: A survey on open-source cloud computing solutions. In: Teyssier, S. (ed.) WCGA—8th Workshop on Clouds, Grids and Applications, Gramado:28 May, 3–16 (2010)Google Scholar
  7. 7.
    Mahjoub, M., Mdhaffar, A., Halima, R.B, Jmaiel, M.: Comparative Study of the Current Cloud Computing Technologies and Offers (2011)Google Scholar
  8. 8.
    Sholler, D.: SOA User Survey: Adoption Trends and Characteristics, Gartner, September 26, p. 32 (2008)Google Scholar
  9. 9.
    Buyya, R., Yeo, C.S., Venugopal, S.: Market-oriented cloud computing: vision, hype, and reality for delivering IT services as computing utilities. In: Proceedings of the 2008 10th IEEE International Conference on High Performance (2008)Google Scholar
  10. 10.
    Tantik, E., Anderl, R.: Potentials of the asset administration shell of Industries 4.0 for service-oriented business models. Procedia CIRP 64, 363–368 (2017)CrossRefGoogle Scholar
  11. 11.
    Kem, O., Balbo, F., Zimmermann, A.: Traveler-oriented advanced traveler information system based on dynamic discovery of resources: potentials and challenges. Transp. Res. Procedia 22, 635–644 (2017)CrossRefGoogle Scholar
  12. 12.
    Stelly, C., Roussev, V.: SCARF: a container-based approach to cloud-scale digital forensic processing. Digital Invest. 22, S39–S47 (2017)CrossRefGoogle Scholar
  13. 13.
    Lu, Y., Ju, F.: Smart manufacturing systems based on Cyber-Physical Manufacturing Services (CPMS). IFAC-Papers on Line 50(1), 15883–15889 (2017)CrossRefGoogle Scholar
  14. 14.
    Kantardzic, M.: Data Mining—Concepts, Models, Methods, and Algorithms. IEEE Press, Wiley-Interscience (2003)zbMATHGoogle Scholar
  15. 15.
    Cheeseman, P., Kelly, J., Self, M., Stutz, J., Taylor, W., Freeman, D.: Autoclass: A Bayesian classification system. In: Machine Learning Proceedings 1988, pp. 54–64. Morgan Kaufmann (1988)Google Scholar
  16. 16.
    Cheeseman, P.C., Self, M., Kelly, J., Taylor, W., Freeman, D., Stutz, J.C.: Bayesian classification. In: AAAI, vol. 88, pp. 607–611 (1988)Google Scholar
  17. 17.
    Shafiq, O., Alhajj, R., Rokne, J.G., Toma, I.: A hybrid technique for dynamic web service discovery. In: 2010 IEEE International Conference on Information Reuse & Integration, pp. 121–125. IEEE (2010)Google Scholar
  18. 18.
    Nisa, R., Qamar, U.: A text mining based approach for web service classification. IseB 13(4), 751–768 (2015)CrossRefGoogle Scholar
  19. 19.
    Zhu, Y., Li, X., Wang, J., Liu, Y., Qu, Z.: Practical secure naïve bayesian classification over encrypted big data in cloud. Int. J. Found. Comput. Sci. 28(06), 683–703 (2017)CrossRefGoogle Scholar
  20. 20.
    Mishra, N., Sharma, T.K., Sharma, V., Vimal, V.: Secure framework for data security in cloud computing. In: Soft Computing: Theories and Applications, pp. 61–71. Springer, Singapore (2018)Google Scholar
  21. 21.
    Pandey, P., Saroliya, A., Kumar, R.: Analyses and detection of health insurance fraud using data mining and predictive modeling techniques. In: Pant, M., Ray, K., Sharma, T., Rawat, S., Bandyopadhyay, A. (eds) Soft computing: theories and applications. Advances in Intelligent Systems and Computing, vol. 584. Springer, Singapore (2018)Google Scholar
  22. 22.
    Bhardwaj, T., Kumar, M., Sharma, S.C.: Megh: a private cloud provisioning various IaaS and SaaS. In: Soft Computing: Theories and Applications, pp. 485–494. Springer, Singapore (2018)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Computer Science and MathematicsMagadh UniversityBodh GayaIndia

Personalised recommendations