Design of a Cloud Brokerage Architecture Using Fuzzy Rough Set Technique

  • Parwat Singh Anjana
  • Rajeev Wankar
  • C. Raghavendra Rao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10607)


Cloud computing offers numerous services to the cloud consumers such as infrastructure, platform, software, etc. Due to the vast diversity in available cloud services from the user point of view, it leads to several challenges to rank and select the potential cloud service. One of the plausible solutions for the problem can be obtained with the use of Rough Set Theory (RST) and available in the literature. Unfortunately, Rough Set Theory cannot deal with numerical values. One of the classical solutions to this problem can be obtained by using Fuzzy Rough Set. To the best of our knowledge, there is no working Fuzzy-Rough Set based brokerage architecture available which is used for minimizing attributes, search space and for ranking the service providers. In this paper, we proposed a Fuzzy Rough Set based Cloud Brokerage (FRSCB) architecture, which is responsible for service selection based on consumers Quality of Service (QoS) request. We propose to use Fuzzy Rough Set Theory (FRST) to minimize the number of attributes and searching space. We also did the QoS attribute categorization to identify functional and non-functional requirements and behavior of the attributes (static/dynamic). Finally, we develop an algorithm that recommends potential cloud services to the cloud consumers.


Cloud computing Quality of Service Cloud brokerage Fuzzy Rough Set Reduct Cloud Service Provider Ranking 


  1. 1.
    Cloud Service Broker. Accessed June 2016
  2. 2.
    Zhao, B., Tung, Y.-K.: Determination of optimal unit hydrographs by linear programming. Water Resour. Manage. 8(2), 101–119 (1994)CrossRefGoogle Scholar
  3. 3.
    Sun, L., Dong, H., Hussain, F.K., Hussain, O.K., Chang, E.: Cloud service selection: state-of-the-art and future research directions. J. Netw. Comput. Appl. 45, 134–150 (2014)CrossRefGoogle Scholar
  4. 4.
    Garg, S.K., Versteeg, S., Buyya, R.: A framework for ranking of cloud computing services. Future Gener. Comput. Syst. 29(4), 1012–1023 (2013)CrossRefGoogle Scholar
  5. 5.
    Cloud Service Measurement Index Consortium (CSMIC), SMI framework. Accessed June 2016
  6. 6.
    Ganghishetti, P., Wankar, R.: Quality of service design in clouds. CSI Commun. 35(2), 12–15 (2011)Google Scholar
  7. 7.
    Ganghishetti, P., Wankar, R., Almuttairi, R.M., Rao, C.R.: Rough set based quality of service design for service provisioning in clouds. In: Yao, J.T., Ramanna, S., Wang, G., Suraj, Z. (eds.) RSKT 2011. LNCS, vol. 6954, pp. 268–273. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-24425-4_36 CrossRefGoogle Scholar
  8. 8.
    Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)CrossRefzbMATHGoogle Scholar
  9. 9.
    Pawlak, Z., Skowron, A.: Rudiments of rough sets. Inf. Sci. 177(1), 3–27 (2007)CrossRefzbMATHMathSciNetGoogle Scholar
  10. 10.
    Skowron, A., Jankowski, A., Swiniarski, R.W.: Foundations of rough sets. In: Kacprzyk, J., Pedrycz, W. (eds.) Springer Handbook of Computational Intelligence, pp. 331–348. Springer, Heidelberg (2015). doi: 10.1007/978-3-662-43505-2_21 CrossRefGoogle Scholar
  11. 11.
    Jensen, R., Shen, Q.: Rough set-based feature selection. In: Rough Computing: Theories, Technologies, p. 70 (2008)Google Scholar
  12. 12.
    Gray, W.D., Boehm-Davis, D.A.: Milliseconds matter: an introduction to microstrategies and to their use in describing and predicting interactive behavior. J. Exp. Psychol.: Appl. 6(4), 322 (2000)Google Scholar
  13. 13.
    Schad, J., Dittrich, J., Quiané-Ruiz, J.-A.: Runtime measurements in the cloud: observing, analyzing, and reducing variance. Proc. VLDB Endow. 3(1–2), 460–471 (2010)CrossRefGoogle Scholar
  14. 14.
    Foster, I., Zhao, Y., Raicu, I., Lu, S.: Cloud computing and grid computing 360-degree compared. In: Grid Computing Environments Workshop, GCE 2008, pp. 1–10. IEEE (2008)Google Scholar
  15. 15.
    Jain, A.K.: Data clustering: 50 years beyond k-means. Pattern Recogn. Lett. 31(8), 651–666 (2010)CrossRefGoogle Scholar
  16. 16.
    Tibshirani, R., Walther, G., Hastie, T.: Estimating the number of clusters in a data set via the gap statistic. J. R. Stat. Soc.: Ser. B (Stat. Methodol.) 63(2), 411–423 (2001)CrossRefzbMATHMathSciNetGoogle Scholar
  17. 17.
    R Core Team: R Language Definition. R Foundation for Statistical Computing, Vienna, Austria (2000)Google Scholar
  18. 18.
    R Development Environment. Accessed June 2016
  19. 19.
    CRAN-package fgui: GUI interface. Accessed June 2016
  20. 20.
    Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw.: Pract. Exp. 41(1), 23–50 (2011)Google Scholar
  21. 21.
    Cloud Harmony. Accessed June 2016
  22. 22.
    Catteddu, D., Hogben, G., et al.: Cloud computing information assurance framework. In: European Network and Information Security Agency (ENISA) (2009)Google Scholar
  23. 23.
    Cloud Security Alliance (CSA): Cloud Control Matrix (CCM). Accessed June 2016
  24. 24.
    Jensen, R., Shen, Q.: New approaches to fuzzy-rough feature selection. IEEE Trans. Fuzzy Syst. 17(4), 824–838 (2009)CrossRefGoogle Scholar
  25. 25.
    Pawlak, Z.: Rough sets. Int. J. Comput. Inf. Sci. 11(5), 341–356 (1982)CrossRefzbMATHGoogle Scholar
  26. 26.
    CRAN-package roughsets. Accessed July 2017
  27. 27.
    Cloud service market: a comprehensive overview of cloud computing services. Accessed June 2016

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Parwat Singh Anjana
    • 1
  • Rajeev Wankar
    • 1
  • C. Raghavendra Rao
    • 1
  1. 1.School of CISUniversity of HyderabadHyderabadIndia

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