Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

A Novel Framework for Cloud Service Evaluation and Selection Using Hybrid MCDM Methods

  • Research Article - Computer Engineering and Computer Science
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

With the rapid growth of cloud services in recent years, it is very difficult to choose the suitable cloud services among those services that provide similar functionality. The non-functional quality of services is considered the most significant factor for appropriate service selection and user satisfaction in cloud computing. However, with a vast diversity in the cloud service, selection of a suitable cloud service is a very challenging task for a customer under an unpredictable environment. This study introduces a computational framework for determining the most suitable candidate cloud service by integrating the analytical hierarchical process (AHP) and Technique for order preference by similarity to ideal solution (TOPSIS). Using AHP, we define the architecture for selection process of cloud services and compute the criteria weights using pairwise comparison. Thereafter, using TOPSIS method, we obtained the final ranking of the cloud service based on overall performance. A real-time cloud case study proves the potential of our proposed framework and methodology, which demonstrates the efficacy by inducing better performance, when compared to other available cloud service selection methodologies. Finally, sensitivity analysis testifies the effectiveness and the correctness of our proposed methodology.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Mell, P.; Grance, T.: The NIST definition of cloud computing. Natl. Inst.Standards Technol. 53(6), 50 (2009)

    Google Scholar 

  2. Rajkumar, B., Yeo, C., Venugopal, S., Broberg, J., Brandic, I.: Cloud computing and emerging it platforms, Future Gener. Comput. Syst. 25(6), 599–616 (2009)

  3. Lecznar, M., Patig, S.: Cloud computing providers: characteristics and recommendations. In: International Conference on E-Technologies, pp. 32–45. Springer (2011)

  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)

    Article  Google Scholar 

  5. McKendrick, J.: Ten companies where SOA made a difference in 2006. Retrieved March 15 (2006) 2010.

  6. Siegel, J., Perdue, J.: Cloud services measures for global use: the service measurement index (SMI). In: 2012 Annual SRII Global Conference (SRII). IEEE, pp. 411–415 (2012)

  7. Jahani, A.; Khanli, L.M.: Cloud service ranking as a multi objective optimization problem. J. Supercomput. 72(5), 1897–1926 (2016)

    Article  Google Scholar 

  8. Jatoth, C.; Gangadharan, G.; Fiore, U.: Evaluating the efficiency of cloud services using modified data envelopment analysis and modified super-efficiency data envelopment analysis. Soft Comput. 21, 1–14 (2016)

    Google Scholar 

  9. Liu, S.; Chan, F.T.; Ran, W.: Decision making for the selection of cloud vendor: an improved approach under group decision-making with integrated weights and objective/subjective attributes. Expert Syst. Appl. 55, 37–47 (2016)

    Article  Google Scholar 

  10. Lee, S.; Seo, K.-K.: A hybrid multi-criteria decision-making model for a cloud service selection problem using BSC, fuzzy Delphi method and fuzzy AHP. Wirel. Pers. Commun. 86(1), 57–75 (2016)

    Article  Google Scholar 

  11. Subramanian, T.; Savarimuthu, N.: Cloud service evaluation and selection using fuzzy hybrid mcdm approach in marketplace. Int. J. Fuzzy Syst. Appl. 5(2), 118–153 (2016)

    Article  Google Scholar 

  12. ChangD, Y.: Application of the extent analysis method on fuzzy AHP. Eur. J. Oper. Res. 95(3), 649–655 (1996)

    Article  Google Scholar 

  13. Hwang, C.-L., Yoon, K.: Multiple Attribute Decision Making, vol. 186. Lecture Notes in Economics and Mathematical Systems (1981)

    Chapter  Google Scholar 

  14. Chen, F.; Dou, R.; Li, M.; Wu, H.: A flexible QoS-aware web service composition method by multi-objective optimization in cloud manufacturing. Comput. Ind. Eng. 99, 423–431 (2016)

    Article  Google Scholar 

  15. Heilig, L.; Lalla-Ruiz, E.; Voß, S.: A cloud brokerage approach for solving the resource management problem in multi-cloud environments. Comput. Ind. Eng. 95, 16–26 (2016)

    Article  Google Scholar 

  16. Zhou, A.; Wang, S.; Li, J.; Sun, Q.; Yang, F.: Optimal mobile device selection for mobile cloud service providing. J. Supercomput. 72(8), 3222–3235 (2016)

    Article  Google Scholar 

  17. 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 

  18. Zhou, A.; Wang, S.; Zheng, Z.; Hsu, C.H.; Lyu, M.R.; Yang, F.: On cloud service reliability enhancement with optimal resource usage. IEEE Trans. Cloud Comput. 4(4), 452–466 (2016)

    Article  Google Scholar 

  19. Godse, M., Mulik, S.: An approach for selecting software-as-a-service (saas) product. In: IEEE International Conference on Cloud Computing, 2009. CLOUD’09, pp. 155–158. IEEE (2009)

  20. Chen, C.-T., Hung, W.-Z., Zhang, W.-Y.: Using intervalvalued fuzzy vikor for cloud service provider evaluation and selection. In: Proceedings of the International Conference on Business and Information (BAI13) (2013)

  21. 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)

    Article  Google Scholar 

  22. Tran, V.X.; Tsuji, H.; Masuda, R.: A new QoS ontology and its QoS-based ranking algorithm for web services. Simul. Model. Pract. Theory 17(8), 1378–1398 (2009)

    Article  Google Scholar 

  23. Lin, S.-Y.; Lai, C.-H.; Wu, C.-H.; Lo, C.-C.: A trustworthy QoS-based collaborative filtering approach for web service discovery. J. Syst. Softw. 93, 217–228 (2014)

    Article  Google Scholar 

  24. Wang, P.: Qos-aware web services selection with intuitionistic fuzzy set under consumers vague perception. Expert Syst. Appl. 36(3), 4460–4466 (2009)

    Article  Google Scholar 

  25. Alhamad, M.; Dillon, T.; Chang, E.: A trust-evaluation metric for cloud applications. Int. J. Mach. Learn. Comput. 1(4), 416 (2011)

    Article  Google Scholar 

  26. Dastjerdi, A.V., Buyya, R.: A taxonomy of QoS management and service selection methodologies for cloud computing. Cloud Comput Methodol Syst Appl. (2011). https://doi.org/10.1201/b11149-8

    Chapter  Google Scholar 

  27. ur Rehman, Z., Hussain, O.K., Hussain, F.K.: Iaas cloud selection using MCDM methods. In: 2012 IEEE Ninth International Conference on e-Business Engineering (ICEBE), pp. 246–251. IEEE (2012)

  28. Roy, B.: The outranking approach and the foundations of electre methods. Theor. Decis. 31(1), 49–73 (1991)

    Article  MathSciNet  Google Scholar 

  29. ur Rehman, Z.; Hussain, O.K.; Hussain, F.K.: Parallel cloud service selection and ranking based on QoS history. Int. J. Parallel Program. 42(5), 820–852 (2014)

    Article  Google Scholar 

  30. Karim, R., Ding, C., Miri, A.: An end-to-end QoS mapping approach for cloud service selection. In: 2013 IEEE Ninth World Congress on Services (SERVICES), pp. 341–348. IEEE (2013)

  31. Chahal, R.K., Singh, S.: Fuzzy logic and AHP-based ranking of cloud service providers. In: Computational Intelligence in Data Mining, vol. 1, pp. 337–346. Springer (2016)

  32. Le, S., Dong, H., Hussain, F.K., Hussain, O.K., Ma, J., Zhang, Y.: Multicriteria decision making with fuzziness and criteria interdependence in cloud service selection. In: 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1929–1936. IEEE (2014)

  33. Satty, T.: The Analytic Hierarchy Process. McGrawHill, New York (1980)

    Google Scholar 

  34. Chan, F.T.; Kumar, N.: Global supplier development considering risk factors using fuzzy extended AHP-based approach. Omega 35(4), 417–431 (2007)

    Article  Google Scholar 

  35. Kulak, O.; Kahraman, C.: Fuzzy multi-attribute selection among transportation companies using axiomatic design and analytic hierarchy process. Inf. Sci. 170(2), 191–210 (2005)

    Article  Google Scholar 

  36. Benitez, J.M.; Martín, J.C.; Román, C.: Using fuzzy number for measuring quality of service in the hotel industry. Tour. Manag. 28(2), 544–555 (2007)

    Article  Google Scholar 

  37. Sangaiah, A.K.; Gopal, J.; Basu, A.; Subramaniam, P.R.: An integrated fuzzy dematel, topsis, and electre approach for evaluating knowledge transfer effectiveness with reference to GSD project outcome. Neural Comput. Appl. 28(1), 111–123 (2017)

    Article  Google Scholar 

  38. Zaidan, A.; Zaidan, B.; Al-Haiqi, A.; Kiah, M.L.M.; Hussain, M.; Abdulnabi, M.: Evaluation and selection of open-source emr software packages based on integrated AHP and topsis. J. Biomed. Inform. 53, 390–404 (2015)

    Article  Google Scholar 

  39. Dweiri, F.; Kumar, S.; Khan, S.A.; Jain, V.: Designing an integrated AHP based decision support system for supplier selection in automotive industry. Expert Syst. Appl. 62, 273–283 (2016)

    Article  Google Scholar 

  40. Cloud Harmony Reports. http://static.lindsberget.se/state-of-the-cloud-compute-0714.pdf [Online; Accessed 12 May 2016]

  41. Saltelli, A.; Chan, K.; Scott, E.M.; et al.: Sensitivity Analysis, vol. 1. Wiley, New York (2000)

    MATH  Google Scholar 

  42. Christopher Frey, H.; Patil, S.R.: Identification and review of sensitivity analysis methods. Risk Anal. 22(3), 553–578 (2002)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, India

    Rakesh Ranjan Kumar, Siba Mishra & Chiranjeev Kumar

Authors
  1. Rakesh Ranjan Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Siba Mishra
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chiranjeev Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rakesh Ranjan Kumar.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kumar, R.R., Mishra, S. & Kumar, C. A Novel Framework for Cloud Service Evaluation and Selection Using Hybrid MCDM Methods. Arab J Sci Eng 43, 7015–7030 (2018). https://doi.org/10.1007/s13369-017-2975-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-017-2975-3

Keywords

Search

Navigation