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Solving cloud vendor selection problem using intuitionistic fuzzy decision framework

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Abstract

This paper presents a new decision-making framework called cloud vendor selector (CVS) for effective selection of cloud vendors by mitigating the challenge of unreasonable criteria weight assignment and improper management of uncertainty. The CVS comprises of two stages where, in the first stage, decision-makers’ intuitionistic fuzzy-valued preferences are aggregated using newly proposed extended simple Atanassov’s intuitionistic weighted geometry operator. Further, in the second stage, criteria weights are estimated by using newly proposed intuitionistic fuzzy statistical variance method and finally, ranking of cloud vendor (CV) is done using newly proposed three-way VIKOR method under intuitionistic fuzzy environment which introduces neutral category along with cost and benefit for better understanding the nature of criteria. An illustrative example of CV selection is demonstrated to show the practicality and usefulness of the proposed framework. Finally, the strength and weakness of the proposal are realized from both theoretic and numeric context by comparison with other methods.

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References

  1. Liu S, Chan FTS, Ran W (2016) 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

    Article  Google Scholar 

  2. Armbrust M, Fox A, Griffith R et al (2010) Above the clouds: a Berkeley view of cloud computing. Commun ACM 53(4):50–58

    Article  Google Scholar 

  3. Garrison G, Wakefield RL, Kim S (2015) The effects of IT capabilities and delivery model on cloud computing success and firm performance for cloud supported processes and operations. Int J Inf Manag 35(4):377–393

    Article  Google Scholar 

  4. Misra SC, Mondal A (2011) Identification of a company’s suitability for the adoption of cloud computing and modelling its corresponding return on investment. Math Comput Model 53(3):504–521

    Article  Google Scholar 

  5. Martens B, Teuteberg F (2012) Decision-making in cloud computing environments: a cost and risk based approach. Inf Syst Front 14(4):871–893

    Article  Google Scholar 

  6. Whaiduzzaman M, Gani A, Anuar NB et al (2014) Cloud service selection using multicriteria decision analysis. Sci World J 2014:1–10

    Google Scholar 

  7. Zheng X, Da Xu L, Chai S (2017) Ranking-based cloud service recommendation. In: 2017 IEEE International conference on edge computing, pp 136–141

  8. Nezarat A, Dastghaibyfard G (2016) A game theoretical model for profit maximization resource allocation in cloud environment with budget and deadline constraints. J Supercomput 72(12):4737–4770

    Article  Google Scholar 

  9. Arman A, Foresti S, Livraga G et al (2016) A consensus-based approach for selecting cloud plans. In: 2016 IEEE 2nd international forum on research and technologies for society and industry leveraging a better tomorrow. RTSI 2016, pp 1–6

  10. Wibowo S, Grandhi S, Wells M et al (2016) A multicriteria group decision making procedure for selecting cloud based ERP system providers. In: 2016 12th international conference on natural computation, fuzzy systems and knowledge discovery, pp 1071–1076

  11. Alabool HM, Mahmood AK (2016) A novel evaluation model for improving trust level of infrastructure as a service. In: 2015 international symposium on mathematical sciences and computing research. iSMSC 2015—Proceedings, pp 162–167

  12. Ogunrinde RR, Jusoh YY, Pa NC et al (2016) Cloud enterprise resource planning selection model for small and medium enterprises. Adv Sci Lett 22(8):1939–1943

    Article  Google Scholar 

  13. Khan MZ, Qamar U (2016) Towards service evaluation and ranking model for cloud infrastructure selection. In: Proceedings—2015 IEEE 12th international conference on ubiquitous intelligence and computing. 2015 IEEE 12th international conference on advanced and trusted computing. 2015 IEEE 15th international conference on scalable computing and communications, pp 1282–1287

  14. Jiang Y, Zhao X (2015) SLA-oriented service selection in cloud environment: a PROMETHEE-based approach. In: 2015 4th international conference on computer science and network technology, pp 872–875

  15. Supriya M, Sangeeta K, Patra GK (2016) Trustworthy cloud service provider selection using multi criteria decision making methods. Eng Lett 24(1):1–10

    Google Scholar 

  16. Kaneko R, Pavarangkoon P, Oki E (2016) Virtual machine selection scheme considering reliability for cloud services. In: 2015 21st Asia-Pacific conference communications. APCC 2015, pp 404–409

  17. Wagle SS, Guzek M, Bouvry P et al (2016) An evaluation model for selecting cloud services from commercially available cloud providers. In: Proceedings—IEEE 7th international conference on cloud computing technology and science. Cloud Com 2015, pp 107–114

  18. Zhu H, Wu L, Huang K et al (2016) Research on methods for discovering and selecting cloud infrastructure services based on feature modeling. Math Probl Eng 1:1–19

    Google Scholar 

  19. Jalloh MM, Zhu S, Fang F et al (2015) On selecting composite network-cloud services: a quality-of-service based approach. In: Proceedings of 2015 conference on research in adaptive and convergent systems, pp 242–246

  20. Menzel M, Ranjan R, Wang L et al (2015) CloudGenius: a hybrid decision support method for automating the migration of web application clusters to public clouds. IEEE Trans Comput 64(5):1336–1348

    Article  MathSciNet  MATH  Google Scholar 

  21. Singh H, Randhawa R (2015) CPSEL: Cloud provider selection framework for ranking and selection of cloud provider. Int J Appl Eng Res 10(7):18787–18810

    Google Scholar 

  22. Tang C, Liu J (2015) Selecting a trusted cloud service provider for your SaaS program. Comput Secur 50:60–73

    Article  Google Scholar 

  23. Li X, He J, Du Y (2015) Trust based service optimization selection for cloud computing. Int J Multimed Ubiquitous Eng 10(5):221–230

    Article  Google Scholar 

  24. Ghosh N, Ghosh SK, Das SK (2015) SelCSP: A framework to facilitate selection of cloud service providers. IEEE Trans Cloud Comput 3(1):66–79

    Article  Google Scholar 

  25. Duckstein L, Opricovic S (1980) Multiobjective optimization in river basin development. Water Resour Res 16(1):14–20

    Article  Google Scholar 

  26. Opricovic S, Tzeng GH (2007) Extended VIKOR method in comparison with outranking methods. Eur J Oper Res 178(2):514–529

    Article  MATH  Google Scholar 

  27. Opricovic S, Tzeng GH (2004) Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS. Eur J Oper Res 156(2):445–455

    Article  MATH  Google Scholar 

  28. Atanassov KT (1986) Intuitionistic fuzzy sets. Fuzzy Sets Syst 20(1):87–96

    Article  MATH  Google Scholar 

  29. Liao H, Xu Z (2015) Consistency of the fused intuitionistic fuzzy preference relation in group intuitionistic fuzzy analytic hierarchy process. Appl Soft Comput 35(10):812–826

    Article  Google Scholar 

  30. Xu Z, Liao H (2015) Intuitionistic fuzzy analytic hierarchy process. IEEE Trans Fuzzy Syst 22(4):1–14

    Google Scholar 

  31. Deschrijver G (2007) Arithmetic operators in interval-valued fuzzy set theory. Inf Sci 177(14):2906–2924

    Article  MathSciNet  MATH  Google Scholar 

  32. Yu PL, Zeleny M (1975) The set of all nondominated solutions in linear cases and a multicriteria simplex method. J Math Anal Appl 49(2):430–468

    Article  MathSciNet  MATH  Google Scholar 

  33. Sayadi MK, Heydari M, Shahanaghi K (2009) Extension of VIKOR method for decision making problem with interval numbers. Appl Math Model 33(5):2257–2262

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  35. Spearman (1904) The proof and measurement of association between two things. Am J Psychol 15(1):72–101

    Article  Google Scholar 

  36. Lima Junior FR, Osiro L, Carpinetti LCR (2014) A comparison between Fuzzy AHP and Fuzzy TOPSIS methods to supplier selection. Appl Soft Comput J 21(8):194–209

    Article  Google Scholar 

Download references

Funding

This study was funded by University Grants Commission (UGC), India (Grant No. F./2015-17/RGNF-2015-17-TAM-83) and Department of Science and Technology (DST), India (Grant No. SR/FST/ETI-349/2013).

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Correspondence to K. Soundarapandian Ravichandran.

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All authors declare that they have no conflict of interest.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Krishankumar, R., Ravichandran, K.S. & Tyagi, S.K. Solving cloud vendor selection problem using intuitionistic fuzzy decision framework. Neural Comput & Applic 32, 589–602 (2020). https://doi.org/10.1007/s00521-018-3648-1

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