Skip to main content
SpringerLink
Log in

Evidence-Based Cloud Vendor Assessment with Generalized Orthopair Fuzzy Information and Partial Weight Data

  • Chapter
  • First Online:
q-Rung Orthopair Fuzzy Sets

Abstract

As the information technology (IT) market booms globally, the urge for technological advancement grows. Cloud computing is a sophisticated technology that offers resources on demand. Due to the increase in computation, firms rely on cloud technology for resource management. Attracted by the abundant need, many cloud vendors evolve in the market, and selecting an apt vendor (CV) becomes complex due to the multiple service factors. Previous studies on CV selection incur lacunae viz., (i) uncertainty was not handled flexibly and (ii) personalized ranking was unavailable based on agent-driven data. Motivated by these lacunae and to glue the same, a scientific model is developed in this paper. A generalized orthopair fuzzy set is adopted for the flexible management of uncertainty and ease of preference sharing. Furthermore, a new mathematical model is formulated for factors’ significance assessment, and an evidence-based approximation approach is proposed for ranking CVs based on agent-driven data. Finally, a real case study of CV adoption by an academic institution is provided with a discussion on the merits and limitations of the model from theoretical and statistical perspectives.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover 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. R. Buyya, C.S. Yeo, S. Venugopal, J. Broberg, I. Brandic, Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility. Futur. Gener. Comput. Syst. 25, 599–616 (2009). https://doi.org/10.1016/j.future.2008.12.001

    Article  Google Scholar 

  2. S.K. Garg, S. Versteeg, R. Buyya, A framework for ranking of cloud computing services. Futur. Gener. Comput. Syst. 29, 1012–1023 (2013). https://doi.org/10.1016/j.future.2012.06.006

    Article  Google Scholar 

  3. C. Jatoth, G.R. Gangadharan, U. Fiore, R. Buyya, SELCLOUD: a hybrid multi-criteria decision-making model for selection of cloud services. Soft Comput. 1–15 (2018). https://doi.org/10.1007/s00500-018-3120-2

  4. G. Garrison, R.L. Wakefield, S. Kim, The effects of IT capabilities and delivery model on cloud computing success and firm performance for cloud supported processes and operations. Int. J. Inf. Manage. 35, 377–393 (2015). https://doi.org/10.1016/j.ijinfomgt.2015.03.001

    Article  Google Scholar 

  5. B. Martens, F. Teuteberg, Decision-making in cloud computing environments: a cost and risk-based approach. Inf. Syst. Front. 14, 871–893 (2012). https://doi.org/10.1007/s10796-011-9317-x

    Article  Google Scholar 

  6. S.C. Misra, A. Mondal, Identification of a company’s suitability for the adoption of cloud computing and modelling its corresponding return on investment. Math. Comput. Model. 53, 504–521 (2011). https://doi.org/10.1016/j.mcm.2010.03.037

    Article  Google Scholar 

  7. M. Whaiduzzaman, A. Gani, N.B. Anuar, M. Shiraz, M.N. Haque, I.T. Haque, Cloud service selection using multicriteria decision analysis. Sci. World J. (2014). https://doi.org/10.1155/2014/459375

  8. L. Sun, H. Dong, F.K. Hussain, O.K. Hussain, E. Chang, Cloud service selection: state-of-the-art and future research directions. J. Netw. Comput. Appl. 45, 134–150 (2014). https://doi.org/10.1016/j.jnca.2014.07.019

    Article  Google Scholar 

  9. R.R. Yager, Generalized orthopair fuzzy sets. IEEE Trans. Fuzzy Syst. 25, 1222–1230 (2017). https://doi.org/10.1109/TFUZZ.2016.2604005

    Article  Google Scholar 

  10. N. Ghosh, S.K. Ghosh, S.K. Das, SelCSP: a framework to facilitate selection of cloud service providers. IEEE Trans. Cloud Comput. 3, 66–79 (2015). https://doi.org/10.1109/TCC.2014.2328578

    Article  Google Scholar 

  11. S. Liu, F.T.S. Chan, W. Ran, Decision making for the selection of cloud vendor: aAn improved approach under group decision-making with integrated weights and objective/subjective attributes. Expert Syst. Appl. 55, 37–47 (2016). https://doi.org/10.1016/j.eswa.2016.01.059

    Article  Google Scholar 

  12. R.R. Kumar, S. Mishra, C. Kumar, Prioritizing the solution of cloud service selection using integrated MCDM methods under fuzzy environment. J. Supercomput. 73, 4652–4682 (2017). https://doi.org/10.1007/s11227-017-2039-1

    Article  Google Scholar 

  13. R.R. Kumar, C. Kumar, A multi criteria decision making method for cloud service selection and ranking. Int. J. Ambient Comput. Intell. 9, 1–14 (2018). https://doi.org/10.4018/IJACI.2018070101

    Article  Google Scholar 

  14. M. Lang, M. Wiesche, H. Krcmar, Criteria for selecting cloud service providers: a Delphi study of quality-of-service attributes. Inf. Manag. 55, 746–758 (2018). https://doi.org/10.1016/j.im.2018.03.004

    Article  Google Scholar 

  15. R. Krishankumar, K.S. Ravichandran, S.K. Tyagi, Solving cloud vendor selection problem using intuitionistic fuzzy decision framework. Neural Comput. Appl. 32, 589–602 (2018). https://doi.org/10.1007/s00521-018-3648-1

    Article  Google Scholar 

  16. M. Masdari, H. Khezri, Service selection using fuzzy multi-criteria decision making: a comprehensive review. Springer, Berlin Heidelberg 12, 2803–2834 (2020). https://doi.org/10.1007/s12652-020-02441-w

    Article  Google Scholar 

  17. A. Al-Faifi, B. Song, M.M. Hassan, A. Alamri, A. Gumaei, A hybrid multi-criteria decision method for cloud service selection from Smart data, Futur. Gener. Comput. Syst. 93, 43–57 (2019). https://doi.org/10.1016/j.future.2018.10.023

  18. S. Ramadass, R. Krishankumar, K.S. Ravichandran, H. Liao, S. Kar, E. Herrera-Viedma, Evaluation of cloud vendors from probabilistic linguistic information with unknown/partial weight values. Appl. Soft Comput. J. 97, 106801 (2020). https://doi.org/10.1016/j.asoc.2020.106801

    Article  Google Scholar 

  19. R. Sivagami, K.S. Ravichandran, R. Krishankumar, V. Sangeetha, S. Kar, X.Z. Gao, D. Pamucar, A scientific decision framework for cloud vendor prioritization under probabilistic linguistic term set context with unknown/partial weight information. Symmetry (Basel). 11(5), 682 (2019). https://doi.org/10.3390/sym11050682

  20. M. Azadi, A. Emrouznejad, F. Ramezani, F.K. Hussain, Efficiency measurement of cloud service providers using network data envelopment analysis. IEEE Trans. Cloud Comput. 32, 1–12 (2019). https://doi.org/10.1109/TCC.2019.2927340

    Article  Google Scholar 

  21. J.H. Dahooie, A.S. Vanaki, N. Mohammadi, Choosing the appropriate system for cloud computing implementation by using the interval-valued intuitionistic fuzzy CODAS multiattribute decision-making method (case study: faculty of new sciences and technologies of Tehran university). IEEE Trans. Eng. Manag. 42, 1–14 (2019). https://doi.org/10.1109/TEM.2018.2884866

    Article  Google Scholar 

  22. M. Sharma, R. Sehrawat, Quantifying SWOT analysis for cloud adoption using FAHP-DEMATEL approach: evidence from the manufacturing sector. J. Enterp. Inf. Manag. 33, 1111–1152 (2020). https://doi.org/10.1108/JEIM-09-2019-0276

    Article  Google Scholar 

  23. A. Hussain, J. Chun, M. Khan, A novel customer-centric methodology for optimal service selection (MOSS) in a cloud environment. Futur. Gener. Comput. Syst. 105, 562–580 (2020). https://doi.org/10.1016/j.future.2019.12.024

    Article  Google Scholar 

  24. A. Hussain, J. Chun, M. Khan, A novel framework towards viable cloud service selection as a service (CSSaaS) under a fuzzy environment. Futur. Gener. Comput. Syst. 104, 74–91 (2020). https://doi.org/10.1016/j.future.2019.09.043

    Article  Google Scholar 

  25. R.R. Yager, N. Alajlan, Approximate reasoning with generalized orthopair fuzzy sets. Inf. Fusion. 38, 65–73 (2017). https://doi.org/10.1016/j.inffus.2017.02.005

    Article  Google Scholar 

  26. J. Wang, R. Zhang, X. Zhu, Z. Zhou, X. Shang, W. Li, Some q-rung orthopair fuzzy Muirhead means with their application to multiattribute group decision making. J. Intell. Fuzzy Syst. 36, 1599–1614 (2019). https://doi.org/10.3233/JIFS-18607

    Article  Google Scholar 

  27. J. Wang, G. Wei, J. Lu, F.E. Alsaadi, T. Hayat, C. Wei, Y. Zhang, Some q-rung orthopair fuzzy Hamy mean operators in multiple attribute decision-making and their application to enterprise resource planning systems selection. Int. J. Intell. Syst. 34, 2429–2458 (2019). https://doi.org/10.1002/int.22155

    Article  Google Scholar 

  28. M. Riaz, A. Razzaq, H. Kalsoom, D. PamuÄŤar, H.M. Athar Farid, Y.M. Chu, q-Rung orthopair fuzzy geometric aggregation operators based on generalized and group-generalized parameters with application to water loss management. Symmetry (Basel). 12, 1236 (2020). https://doi.org/10.3390/SYM12081236

  29. P. Liu, S.M. Chen, P. Wang, The g-rung orthopair fuzzy power Maclaurin symmetric mean operators, in 2018 10th International Conference on Advanced Computational Intelligence (ICACI), vol. 10, pp. 156–161. (2018). https://doi.org/10.1109/ICACI.2018.8377599

  30. P. Liu, J. Liu, Some q-rung orthopai fuzzy bonferroni mean operators and their application to multi-attribute group decision making. Int. J. Intell. Syst. 33, 315–347 (2018). https://doi.org/10.1002/int.21933

    Article  Google Scholar 

  31. H. Garg, A novel trigonometric operation-based q-rung orthopair fuzzy aggregation operator and its fundamental properties. Neural Comput. Appl. 32, 15077–15099 (2020). https://doi.org/10.1007/s00521-020-04859-x

    Article  Google Scholar 

  32. H. Garg, S.M. Chen, Multiattribute group decision making based on neutrality aggregation operators of q-rung orthopair fuzzy sets. Inf. Sci. (NY) 517, 427–447 (2020). https://doi.org/10.1016/j.ins.2019.11.035

    Article  MathSciNet  MATH  Google Scholar 

  33. X. Peng, J. Dai, H. Garg, Exponential operation and aggregation operator for q-rung orthopair fuzzy set and their decision-making method with a new score function. Int. J. Intell. Syst. 33, 2255–2282 (2018). https://doi.org/10.1002/int.22028

    Article  Google Scholar 

  34. M. Riaz, H. Garg, H.M.A. Farid, M. Aslam, Novel q-rung orthopair fuzzy interaction aggregation operators and their application to low-carbon green supply chain management. J. Intell. Fuzzy Syst. 41(2), 4109–4126 (2021). https://doi.org/10.3233/jifs-210506

    Article  Google Scholar 

  35. Z. Yang, H. Garg, Interaction Power Partitioned Maclaurin symmetric mean operators under q-rung orthopair incertain linguistic information. Int. J. Fuzzy Syst. 40815 (2021). https://doi.org/10.1007/s40815-021-01062-5

  36. H. Garg, New exponential operation laws and operators for interval-valued q-rung orthopair fuzzy sets in group decision making process. Neural Comput. Appl. 33(20), 13937–13963 (2021). https://doi.org/10.1007/s00521-021-06036-0

    Article  Google Scholar 

  37. W.S. Du, Minkowski-type distance measures for generalized orthopair fuzzy sets. Int. J. Intell. Syst. 33, 802–817 (2018). https://doi.org/10.1002/int.21968

    Article  Google Scholar 

  38. X. Peng, R. Krishankumar, K.S. Ravichandran, Generalized orthopair fuzzy weighted distance-based approximation (WDBA) algorithm in emergency decision-making. Int. J. Intell. Syst. 34, 2364–2402 (2019). https://doi.org/10.1002/int.22140

    Article  Google Scholar 

  39. T. Mahmood, Z. Ali, Entropy measure and TOPSIS method based on correlation coefficient using complex q-rung orthopair fuzzy information and its application to multi-attribute decision making. Soft Comput. 25, 1249–1275 (2021). https://doi.org/10.1007/s00500-020-05218-7

    Article  MATH  Google Scholar 

  40. L. Liu, J. Wu, G. Wei, C. Wei, J. Wang, Y. Wei, Entropy-based GLDS method for social capital selection of a PPP project with q-Rung orthopair fuzzy information. Entropy 22, 414 (2020). https://doi.org/10.3390/E22040414

    Article  MathSciNet  Google Scholar 

  41. R. Krishankumar, S. Nimmagadda, A. Mishra, P. Rani, K.S. Ravichandran, A.H. Gandomi, Solving renewable energy source selection problems using a q-rung orthopair fuzzy-based integrated decision-making approach. J. Clean. Prod. 279, 123329 (2020). https://doi.org/10.1016/j.ygyno.2016.04.081

    Article  Google Scholar 

  42. R. Krishankumar, V. Sangeetha, P. Rani, K.S. Ravichandran, A.H. Gandomi, Selection of apt renewable energy source for smart cities using generalized orthopair fuzzy information, in: 2020 IEEE Symposium Series on Computational Intelligence Canberra Australia, vol. 42, pp. 2861–2868 (2020). https://doi.org/10.1109/ssci47803.2020.9308365

  43. R. Krishankumar, Y. Gowtham, I. Ahmed, K.S. Ravichandran, S. Kar, Solving green supplier selection problem using q-rung orthopair fuzzy-based decision framework with unknown weight information. Appl. Soft Comput. J. 94, 106431 (2020). https://doi.org/10.1016/j.asoc.2020.106431

    Article  Google Scholar 

  44. R. Krishankumar, K.S. Ravichandran, S. Kar, F. Cavallaro, E.K. Zavadskas, A. Mardani, Scientific decision framework for evaluation of renewable energy sources under q-rung orthopair fuzzy set with partially known weight information. Sustain. 11, 1–21 (2019). https://doi.org/10.3390/su11154202

    Article  Google Scholar 

  45. Y. Donyatalab, E. Farrokhizadeh, S.A. Seyfi Shishavan, Similarity measures of q-rung orthopair fuzzy sets based on square root cosine similarity function. Adv. Intell. Syst. Comput. 1197, 475–483 AISC (2021). https://doi.org/10.1007/978-3-030-51156-2_55

  46. X. Peng, L. Liu, Information measures for q-rung orthopair fuzzy sets. Int. J. Intell. Syst. 34, 1795–1834 (2019). https://doi.org/10.1002/int.22115

    Article  Google Scholar 

  47. N. Jan, L. Zedam, T. Mahmood, E. Rak, Z. Ali, Generalized dice similarity measures for q-rung orthopair fuzzy sets with applications. Complex Intell. Syst. 6, 545–558 (2020). https://doi.org/10.1007/s40747-020-00145-4

    Article  Google Scholar 

  48. D. Liu, X. Chen, D. Peng, Some cosine similarity measures and distance measures between q-rung orthopair fuzzy sets. Int. J. Intell. Syst. 34, 1572–1587 (2019). https://doi.org/10.1002/int.22108

    Article  Google Scholar 

  49. P. Liu, T. Mahmood, Z. Ali, Complex q-rung orthopair fuzzy aggregation operators and their applications in multi-attribute group decision making. Information 11 (2020). https://doi.org/10.3390/info11010005

  50. M. Lin, X. Li, L. Chen, Linguistic q-rung orthopair fuzzy sets and their interactional partitioned Heronian mean aggregation operators. Int. J. Intell. Syst. 35, 217–249 (2020). https://doi.org/10.1002/int.22136

    Article  Google Scholar 

  51. B.P. Joshi, A. Singh, P.K. Bhatt, K.S. Vaisala, Interval-valued q -rung orthopair fuzzy sets and their properties. J. Intell. Fuzzy Syst. 35, 5225–5230 (2018). https://doi.org/10.3233/JIFS-169806

    Article  Google Scholar 

  52. H. Garg, A new possibility degree measure for interval-valued q-rung orthopair fuzzy sets in decision-making. Int. J. Intell. Syst. 36, 526–557 (2021). https://doi.org/10.1002/int.22308

    Article  Google Scholar 

  53. H. Garg, CN-q-ROFS: connection number-based q-rung orthopair fuzzy set and their application to decision-making process. Int. J. Intell. Syst. 36(7), 3106–3143 (2021)

    Article  Google Scholar 

  54. X. Peng, Z. Luo, A review of q-rung orthopair fuzzy information: Bibliometrics and future directions. Springer, Netherlands 54, 3361–3430 (2021). https://doi.org/10.1007/s10462-020-09926-2

    Article  Google Scholar 

  55. K.T. Atanassov, Intuitionistic fuzzy sets. Fuzzy Sets Syst. 20, 87–96 (1986). https://doi.org/10.1016/S0165-0114(86)80034-3

    Article  MATH  Google Scholar 

  56. R.R. Yager, Pythagorean membership grades in multicriteria decision making. IEEE Trans. Fuzzy Syst. 22, 958–965 (2014). https://doi.org/10.1109/TFUZZ.2013.2278989

    Article  Google Scholar 

  57. K. Sentz, S. Ferson, Combination of evidence in Dempster-Shafer theory. (2002)

    Google Scholar 

  58. F. Voorbraak, A computationally efficient approximation of Dempster-Shafer theory. Int. J. Man. Mach. Stud. 30, 525–536 (1989). https://doi.org/10.1016/S0020-7373(89)80032-X

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, TN, India

    R. Krishankumar

  2. Department of Logistics, Military Academy, University of Defence, 11000, Belgrade, Serbia

    Dragan Pamucar

  3. Rajiv Gandhi National Institute of Youth Development, Sriperumbudur, TN, India

    K. S. Ravichandran

Authors
  1. R. Krishankumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dragan Pamucar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. K. S. Ravichandran
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to K. S. Ravichandran .

Editor information

Editors and Affiliations

  1. Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Harish Garg

Ethics declarations

Compliance with Ethical Standards

  • Conflict of Interest All authors declare that they have no conflict of interest.

  • Ethical Approval This article does not contain any studies with human participants or animals performed by any authors.

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Krishankumar, R., Pamucar, D., Ravichandran, K.S. (2022). Evidence-Based Cloud Vendor Assessment with Generalized Orthopair Fuzzy Information and Partial Weight Data. In: Garg, H. (eds) q-Rung Orthopair Fuzzy Sets. Springer, Singapore. https://doi.org/10.1007/978-981-19-1449-2_8

Download citation

Publish with us

Policies and ethics

Search

Navigation