With the advancement and expansion of cloud computing services, selecting a proper cloud service is regarded as a challenging endeavor, since there is fierce competition in the industry for delivering a quality experience to its customers. Several Multi-Criteria Decision Making (MCDM) procedures have been adopted by decision makers over the past years to identify the best cloud services, however these approaches suffer from rank reversal and inconsistent results. For addressing these challenges, we introduced a framework integrating the Full Consistency Method (FUCOM) and Method based on the removal effects of criteria (MEREC) for weight estimation and TOmada de Decisao Interativa Multicriterio (TODIM) for ranking the alternatives in a pentagonal neutrosophic environment to determine the optimal cloud service. Comparative study and sensitivity analysis are done to check the efficacy and robustness of our model. The results demonstrate that, in comparison to current methods, the suggested model performs better and produces more consistent results.
Abdel-BassetM.MohamedM.HussienA. N.SangaiahA. K. (2018). A novel group decision-making model based on triangular neutrosophic numbers. Soft Computing, 22(20), 6629–6643. https://doi.org/10.1007/s00500-017-2758-5
2.
Al-MasriE.MahmoudQ. H. (2008). Investigating web services on the world wide web. Proceedings of the 17th international conference on World Wide Web, pp. 795–804.
3.
AlamA. (2022). Cloud-based e-learning: Scaffolding the environment for adaptive e-learning ecosystem based on cloud computing infrastructure. In Computer communication, networking and IoT: Proceedings of 5th ICICC 2021 (Vol. 2, pp. 1–9). Springer Nature Singapore.
4.
AliM.HussainZ.YangM. S. (2022). Hausdorff distance and similarity measures for single-valued neutrosophic sets with application in multi-criteria decision making. Electronics, 12(1), 201. https://doi.org/10.3390/electronics12010201
5.
AndrejićM.PajićV.KilibardaM. (2023). Distribution channel selection using FUCOM-ADAM: A novel approach. Sustainability, 15(19), 14527. https://doi.org/10.3390/su151914527
6.
AttyaM.SakrA. S.AbdulkaderH. M.Al-ShowaikhF.El-SayedM. K. (2023). Novel framework for selecting cloud provider using neutrosophic and modified GAN. Appl. Math, 17(2), 293–307. https://doi.org/10.18576/amis/170212
7.
BaşhanV.DemirelH.GulM. (2020). An FMEA-based TOPSIS approach under single valued neutrosophic sets for maritime risk evaluation: The case of ship navigation safety. Soft Computing, 24(24), 18749–18764. https://doi.org/10.1007/s00500-020-05108-y
8.
ChaiJ. S.SelvachandranG.SmarandacheF.GerogiannisV. C.SonL. H.BuiQ. T.VoB. (2021). New similarity measures for single-valued neutrosophic sets with applications in pattern recognition and medical diagnosis problems. Complex & Intelligent Systems, 7(2), 703–723. https://doi.org/10.1007/s40747-020-00220-w
9.
ChakrabortyA.BroumiS.SinghP. K. (2019). Some properties of pentagonal neutrosophic numbers and its applications in transportation problem environment. Infinite Study, 28(1), 200–215. https://doi.org/10.5281/zenodo.3382542
10.
ChaurasiyaR.JainD. (2023). Hybrid MCDM method on pythagorean fuzzy set and its application. Decision Making: Applications in Management and Engineering, 6(1), 379–398. https://doi.org/10.31181/dmame0306102022c
11.
ChiuW. Y.TzengG. H.LiH. L. (2013). A new hybrid MCDM model combining DANP with VIKOR to improve e-store business. Knowledge-based Systems, 37, 48–61. https://doi.org/10.1016/j.knosys.2012.06.017
12.
DasS. K.ChakrabortyA. (2021). A new approach to evaluate linear programming problem in pentagonal neutrosophic environment. Complex & Intelligent Systems, 7(1), 101–110. https://doi.org/10.1007/s40747-020-00181-0
13.
DeepaN.GanesanK.SrinivasanK.ChangC. Y. (2019). Realizing sustainable development via modified integrated weighting MCDM model for ranking agrarian dataset. Sustainability, 11(21), 6060. https://doi.org/10.3390/su11216060
14.
DemirG.DamjanovićM.MatovićB.VujadinovićR. (2022). Toward sustainable urban mobility by using fuzzy-FUCOM and fuzzy-CoCoSo methods: The case of the SUMP Podgorica. Sustainability, 14(9), 4972. https://doi.org/10.3390/su14094972
15.
DingS.WangZ.WuD.OlsonD. L. (2017). Utilizing customer satisfaction in ranking prediction for personalized cloud service selection. Decision Support Systems, 93, 1–10. https://doi.org/10.1016/j.dss.2016.09.001
16.
EcerF.PamucarD. (2022). A novel LOPCOW-DOBI multi-criteria sustainability performance assessment methodology: An application in developing country banking sector. Omega, 112, 102690. https://doi.org/10.1016/j.omega.2022.102690
17.
GhoruiN.MondalS. P.ChatterjeeB.GhoshA.PalA.DeD.GiriB. C. (2023). Selection of cloud service providers using MCDM methodology under intuitionistic fuzzy uncertainty. Soft Computing, 27(5), 2403–2423. https://doi.org/10.1007/s00500-022-07772-8
18.
GireeshaO.SomuN.KrithivasanK.VSS. S. (2020). IIVIFS-WASPAS: An integrated multi-criteria decision-making perspective for cloud service provider selection. Future Generation Computer Systems, 103(1), 91–110. https://doi.org/10.1016/j.future.2019.09.053.
19.
GomesL. F. A. M. (2009). An application of the TODIM method to the multicriteria rental evaluation of residential properties. European Journal of Operational Research, 193(1), 204–211. https://doi.org/10.1016/j.ejor.2007.10.046
20.
HayyolalamV.KazemA. A. P. (2018). A systematic literature review on QoS-aware service composition and selection in cloud environment. Journal of Network and Computer Applications, 110, 52–74. https://doi.org/10.1016/j.jnca.2018.03.003
21.
HazraA.DontaP. K.AmgothT.DustdarS. (2022). Cooperative transmission scheduling and computation offloading with collaboration of fog and cloud for industrial IoT applications. IEEE Internet of Things Journal, 10(5), 3944–3953. https://doi.org/10.1109/JIOT.2022.3150070
22.
JatothC.GangadharanG. R.FioreU.BuyyaR. (2019). SELCLOUD: A hybrid multi-criteria decision-making model for selection of cloud services. Soft Computing, 23(13), 4701–4715. https://doi.org/10.1007/s00500-018-3120-2.
23.
KahnemanD.TverskyA. (2013). Prospect theory: An analysis of decision under risk. In Handbook of the fundamentals of financial decision making: Part I (pp. 99–127). World Scientific Publishing.
24.
KarimR.DingC.MiriA. (2013). An end-to-end QoS mapping approach for cloud service selection. 2013 IEEE ninth world congress on services, IEEE, pp. 341–348.
25.
Keshavarz-GhorabaeeM.AmiriM.ZavadskasE. K.TurskisZ.AntuchevicieneJ. (2021). Determination of objective weights using a new method based on the removal effects of criteria (MEREC). Symmetry, 13(no. 4), 525. https://doi.org/10.3390/sym13040525
26.
KrishankumarR.RavichandranK. S.TyagiS. K. (2020). Cloud vendor selection problem using intuitionistic fuzzy decision framework. Neural Computing and Applications, 32(4), 589–602. https://doi.org/10.1007/s00521-018-3648-1.
27.
KumarR. R.KumariB.KumarC. (2021). CCS-OSSR: A framework based on hybrid MCDM for optimal service selection and ranking of cloud computing services. Cluster Computing, 24(2), 867–883. https://doi.org/10.1007/s10586-020-03166-3
28.
KumarR. R.MishraS.KumarC. (2017). Prioritizing the solution of cloud service selection using integrated MCDM methods under fuzzy environment. The Journal of Supercomputing, 73(11), 4652–4682. https://doi.org/10.1007/s11227-017-2039-1.
29.
KumarR. R.ShameemM.KumarC. (2022). A computational framework for ranking prediction of cloud services under fuzzy environment. Enterprise Information Systems, 16(1), 167–187. https://doi.org/10.1080/17517575.2021.1889037
30.
LuoM.ZhangG.WuL. (2022). A novel distance between single valued neutrosophic sets and its application in pattern recognition. Soft Computing, 26(21), 11129–11137. https://doi.org/10.1007/s00500-022-07407-y
31.
MishraA. R.SahaA.RaniP.HezamI. M.ShrivastavaR.SmarandacheF. (2022). An integrated decision support framework using single-valued-MEREC-MULTIMOORA for low carbon tourism strategy assessment. Ieee Access, 10(3), 24411–24432. https://doi.org/10.1109/ACCESS.2022.3155171.
32.
MonikaO. P.Sangwan (2022). A framework for evaluating cloud computing services using AHP and TOPSIS approaches with interval valued spherical fuzzy sets. Cluster Computing, 25(6), 4383–4396. https://doi.org/10.1007/s10586-022-03679-z
33.
MudassarM.ZhaiY.LejianL. (2022). Adaptive fault-tolerant strategy for latency-aware IoT application executing in edge computing environment. IEEE Internet of Things Journal, 9(15), 13250–13262. https://doi.org/10.1109/JIOT.2022.3144026
34.
NathS.DasP.DebnathP. (2022). A brief review on multi-attribute decision making in the emerging fields of computer science. In International conference on computational intelligence in communications and business analytics (pp. 3–18). Springer International Publishing.
35.
NathS.DasP.DebnathP. (2023). A Single-valued Pentagonal Neutrosophic Geometric Programming Approach to Optimize Decision Maker’s Satisfaction Level. International Journal of Advanced Computer Science and Applications, 14(4). https://doi.org/10.14569/IJACSA.2023.0140439
36.
NawazF.AsadabadiM. R.JanjuaN. K.HussainO. K.ChangE.SaberiM. (2018). An MCDM method for cloud service selection using a markov chain and the best-worst method. Knowledge-Based Systems, 159, 120–131. https://doi.org/10.1016/j.knosys.2018.06.010
37.
NguyenH.MinhT.NguyenV. P.NguyenD. T. (2023). Selecting cloud database services provider through multi-attribute group decision making: A probabilistic uncertainty linguistics TODIM model. Applied Mathematics in Science and Engineering, 31(1), 2156502. https://doi.org/10.1080/27690911.2022.2156502
38.
OtayI.YıldızT. (2020). Multi-criteria cloud computing service provider selection employing pythagorean fuzzy AHP and VIKOR. In International conference on intelligent and fuzzy systems (pp. 423–431). Springer International Publishing.
39.
PamucarD.EcerF.DeveciM. (2021). Assessment of alternative fuel vehicles for sustainable road transportation of United States using integrated fuzzy FUCOM and neutrosophic fuzzy MARCOS methodology. Science of the Total Environment, 788, 147763. https://doi.org/10.1016/j.scitotenv.2021.147763
40.
PamucarD.EcerF.GligorićZ.GligorićM.DeveciM. (2023). A Novel WENSLO and ALWAS multicriteria methodology and its application to green growth performance evaluation. IEEE Transactions on Engineering Management, 71(1), 9510–9525. https://doi.org/10.1109/TEM.2023.3321697
41.
PamučarD.StevićŽSremacS. (2018). A new model for determining weight coefficients of criteria in mcdm models: Full consistency method (fucom). Symmetry, 10(9), 393. https://doi.org/10.3390/sym10090393
42.
PopovićV.PamučarD.StevićŽLukovacV.JovkovićS. (2022). Multicriteria optimization of logistics processes using a grey FUCOM-SWOT model. Symmetry, 14(4), 794. https://doi.org/10.3390/sym14040794
43.
RanjanM. J.KumarB. P.BhavaniT. D.PadmavathiA. V.BakkaV. (2023). Probabilistic linguistic q-rung orthopair fuzzy archimedean aggregation operators for group decision-making. Decision Making: Applications in Management and Engineering, 6(2), 639–667. https://doi.org/10.31181/dmame622023527
44.
SidhuJ.SinghS. (2017). Design and comparative analysis of MCDM-based multi-dimensional trust evaluation schemes for determining trustworthiness of cloud service providers. Journal of Grid Computing, 15(2), 197–218. https://doi.org/10.1007/s10723-017-9396-0
45.
SidhuJ.SinghS. (2019). Using the improved PROMETHEE for selection of trustworthy cloud database servers, int. Arab J. Inf. Technol, 16(2), 194–202.
46.
SiegelJ.PerdueJ. (2012). Cloud services measures for global use: The service measurement index (SMI). 2012 Annual SRII global conference, IEEE, pp. 411–415.
47.
SimicV.GokasarI.DeveciM.ŠvadlenkaL. (2022). Mitigating climate change effects of urban transportation using a type-2 neutrosophic MEREC-MARCOS model.IEEE Transactions on Engineering Management, 71(8), 3233–3249. https://doi.org/10.1109/TEM.2022.3207375
48.
SmarandacheF. (1999). A unifying field in logics: Neutrosophic logic. Philosophy, American Research Press, pp. 1–141.
49.
StevićŽPamučarD.PuškaA.ChatterjeeP. (2020). Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to COmpromise solution (MARCOS). Computers & Industrial Engineering, 140, 106231. https://doi.org/10.1016/j.cie.2019.106231
50.
SunL.DongH.HussainO. K.HussainF. K.LiuA. X. (2019). A framework of cloud service selection with criteria interactions. Future Generation Computer Systems, 94, 749–764. https://doi.org/10.1016/j.future.2018.12.005
51.
SunL.MaJ.ZhangY.DongH.HussainF. K. (2016). Cloud-FuSeR: Fuzzy ontology and MCDM based cloud service selection. Future Generation Computer Systems, 57, 42–55. https://doi.org/10.1016/j.future.2015.11.025
52.
SunM.ZangT.XuX.WangR. (2013). Consumer-centered cloud services selection using AHP. 2013 International conference on service sciences (ICSS), IEEE, pp. 1–6.
53.
TešićD.RadovanovićM.BožanićD.PamucarA.MilićA.PuškaA. (2022). Modification of the DIBR and MABAC methods by applying rough numbers and its application in making decisions. Information, 13(8), 353. https://doi.org/10.3390/info13080353
54.
TiwariR. K.KumarR. (2022). A framework for prioritizing cloud services in neutrosophic environment. Journal of King Saud University-Computer and Information Sciences, 34(6), 3151–3166. https://doi.org/10.1016/j.jksuci.2020.05.009
55.
TiwariR. K.KumarR.BaranwalG.BuyyaR. (2023). Decision making framework for heterogeneous QoS information: An application to cloud service selection. Journal of Ambient Intelligence and Humanized Computing, 14(3), 2915–2934. https://doi.org/10.1007/s12652-023-04532-w
56.
TomarA.KumarR. R.GuptaI. (2023). Decision making for cloud service selection: A novel and hybrid MCDM approach. Cluster Computing, 26(6), 3869–3887. https://doi.org/10.1007/s10586-022-03793-y
57.
WangH.SmarandacheF.ZhangY.SunderramanR. (2010). Single valued neutrosophic sets. Infinite study, 4(10), 410–413.
58.
WangL.WangY. M.MartínezL. (2020). Fuzzy TODIM method based on alpha-level sets. Expert Systems with Applications, 140, 112899. https://doi.org/10.1016/j.eswa.2019.112899
59.
WuQ.LiuX.QinJ.ZhouL.MardaniA.DeveciM. (2022). An integrated generalized TODIM model for portfolio selection based on financial performance of firms. Knowledge-Based Systems, 249, 108794. https://doi.org/10.1016/j.knosys.2022.108794
60.
YeJ. (2015). Trapezoidal neutrosophic set and its application to multiple attribute decision-making. Neural Computing and Applications, 26(5), 1157–1166. https://doi.org/10.1007/s00521-014-1787-6
61.
YoussefA. E. (2020). An integrated MCDM approach for cloud service selection based on TOPSIS and BWM. IEEE Access, 8, 71851–71865. https://doi.org/10.1109/ACCESS.2020.2987111
62.
Zamani-SabziH.KingJ. P.GardC. C.AbuduS. (2016). Statistical and analytical comparison of multi-criteria decision-making techniques under fuzzy environment. Operations Research Perspectives, 3, 92–117. https://doi.org/10.1016/j.orp.2016.11.001
63.
ZhaoM.RabieiK. (2023). Feasibility of implementing the human resource payroll management system based on cloud computing. Kybernetes, 52(4), 1245–1268. https://doi.org/10.1108/K-07-2021-0554
64.
ŽižovićM.PamucarD. (2019). New model for determining criteria weights: Level based weight assessment (LBWA) model. Decision Making: Applications in Management and Engineering, 2(2), 126–137. https://doi.org/10.31181/dmame1902102z
