Skip to main content

A Fuzz Logic-Based Cloud Computing Provider’s Evaluation and Recommendation Model

  • Conference paper
  • First Online:
Recent Trends in Data Science and Soft Computing (IRICT 2018)

Abstract

The growing number of cloud computing providers has made the selection process difficult and time consuming. This motivates many research to propose cloud computing recommendation system. However, generating an overall rating of a provider based on accumulation of several performance parameters involves uncertainties especially when the computation comprises network-related performance parameters. Hence, this paper proposes a fuzzy logic-based model that evaluates providers based on downlink, uplink and latency parameters, and determines the best provider. The model was constructed based on historical data of the real cloud computing providers implementation. The model was compared against non-fuzzy model, and it showed that the proposed fuzzy logic-based model is better in terms of accuracy. This work implies faster and accurate selection of cloud computing provider.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Duan, Q.: Cloud service performance evaluation: status, challenges, and opportunities–a survey from the system modeling perspective. Digit. Commun. Netw. 3(2), 101–111 (2017)

    Article  Google Scholar 

  2. Mamoun, M.H., Ibrahim, E.M.: A proposed framework for ranking and reservation of cloud services. Int. J. Eng. Technol. 4(9), 536–641 (2014)

    Google Scholar 

  3. RightScale 2017 State of the Cloud Report. https://www.rightscale.com/lp/2017-state-of-the-cloud-report

  4. Zain, T., Aslam, M., Imran, M.R., Martinez-Enriquez, A.M.: Cloud service recommender system using clustering. In: 2014 11th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE), Campeche, pp. 1–6 (2014)

    Google Scholar 

  5. Aznoli, F., Navimipour, N.J.: Cloud services recommendation. J. Netw. Comput. Appl. 77, 73–86 (2017)

    Article  Google Scholar 

  6. Zhang, M., Ranjan, R., Menzel, M., Nepal, S., Strazdins, P., Jie, W., Wang, L.: An infrastructure service recommendation system for cloud applications with real-time QoS requirement constraints. IEEE Syst. J. 11(4), 2960–2970 (2017)

    Article  Google Scholar 

  7. Tchernykha, A., Schwiegelsohn, U., Alexandrov, V., Talbi, E.: Towards understanding uncertainty in cloud computing resource provisioning. Proc. Comput. Sci. 51, 1772–1781 (2015)

    Article  Google Scholar 

  8. Fard, H.M., Ristov, S., Prodan, R.: Handling the uncertainty in resource performance for executing workflow applications in clouds. In: Proceedings of the 9th International Conference on Utility and Cloud Computing, Shanghai, pp. 89–98 (2016)

    Google Scholar 

  9. Minarolli, D., Mazrekaj, A., Freisleben, B.: Tackling uncertainty in long-term predictions for host overload and underload detection in cloud computing. J. Cloud Comput. Adv. Syst. Appl. (2017). https://doi.org/10.1186/s13677-017-0074-3

    Article  Google Scholar 

  10. Garg, S.K., Versteeg, S., Buyya, R.: A framework for ranking of cloud computing services. Future Gener. Comput. Syst. 29(4), 1012–1023 (2013)

    Article  Google Scholar 

  11. Zheng, Z., Zhang, Y., Iyu, M.R.: CloudRank: a QoS-driven component ranking framework for cloud computing. In: 29th IEEE Symposium on Reliable Distributed Systems (2010)

    Google Scholar 

  12. Zadeh, L.A.: The role of fuzzy logic in the management of uncertainty in expert systems. Fuzzy Sets Syst. 11(1–3), 199–227 (1983)

    Article  MathSciNet  Google Scholar 

  13. Supriya, M., Venkataramana, L.J., Sangeeta, K., Patra, G.K.: Estimating trust value for cloud service providers using fuzzy logic. Int. J. Comput. Appl. 48(19), 28–34 (2012)

    Google Scholar 

  14. Wang, S., Liu, Z., Sun, Q., Zou, H., Yang, F.: Towards an accurate evaluation of quality of cloud service in service-oriented cloud computing. J. Intell. Manuf. 25(2), 283–291 (2014)

    Article  Google Scholar 

  15. Klir, G., Yuan, B.: Fuzzy Sets and Fuzzy Logic, vol. 4. Prentice-Hall, Prentice (1995)

    MATH  Google Scholar 

  16. Performance measurement of Cloud Computing services: https://arxiv.org/abs/1205.1622

  17. CloudHarmony: https://cloudharmony.com/

  18. Tallamraju, S.R.: Trust metrics for cloud computing (2013)

    Google Scholar 

  19. Wang, K., Wang, B., Peng, L.: CVAP: validation for cluster analyses. Data Sci. J. 8, 88–93 (2009)

    Article  Google Scholar 

  20. Guillaume, S.: Designing fuzzy inference systems from data: an interpretability oriented review. IEEE Trans. Fuzzy Syst. 9, 426–443 (2001)

    Article  Google Scholar 

  21. Jang, J.-S.R.: Self-learning fuzzy controllers based on temporal back propagation. IEEE Trans. Neural Netw. 3, 714–723 (1992)

    Article  Google Scholar 

  22. Simhachalam, B., Ganesan, G.: Performance comparison of fuzzy and non-fuzzy classification methods. Egypt. Inf. J. 17(2), 183–188 (2016)

    Article  Google Scholar 

  23. Wang, L., Wang, J.: Feature Weighting fuzzy clustering integrating rough sets and shadowed sets. Int. J. Pattern Recogn. Artif. Intell. 26(4), 1769–1776 (2012)

    MathSciNet  MATH  Google Scholar 

  24. Castillo, O., Melin, P.: Design of intelligent systems with interval type-2 fuzzy logic. Type-2 Fuzzy Logic: Theory and Applications - Studies in Fuzziness and Soft Computing, vol. 223, pp. 53–76. Springer, Berlin (2008)

    Chapter  Google Scholar 

  25. Tay, K.M., Lim, C.P.: Optimization of Gaussian fuzzy membership functions and evaluation of the monotonicity property of fuzzy inference systems. In: 2011 IEEE International Conference on Fuzzy Systems (2011)

    Google Scholar 

  26. Zadeh, L.A.: Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans. Syst. Man Cybern. 1, 28–44 (1973)

    Article  MathSciNet  Google Scholar 

  27. Bolón-Canedo, V., Sánchez-Maroño, N., Alonso-Betanzos, A.: A review of feature selection methods on synthetic data. Knowl. Inf. Syst. 34(3), 483–519 (2013)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mohd Hilmi Hasan .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Hasan, M.H., Aziz, N.A., Akhir, E.A.P., Burhan, N.Z.N.M., Razali, R. (2019). A Fuzz Logic-Based Cloud Computing Provider’s Evaluation and Recommendation Model. In: Saeed, F., Gazem, N., Mohammed, F., Busalim, A. (eds) Recent Trends in Data Science and Soft Computing. IRICT 2018. Advances in Intelligent Systems and Computing, vol 843. Springer, Cham. https://doi.org/10.1007/978-3-319-99007-1_22

Download citation

Publish with us

Policies and ethics