Volume 21 , Issue 3 , PP: 88-96, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Ehab R. Mohamed 1 * , Mohamed H. Azazzy 2 , Marwa M. Abdella 3
Doi: https://doi.org/10.54216/IJNS.210308
By automating several formerly labor-intensive processes, like setting up a company's communications network, cloud computing technology (CCT) offers significant benefits to organizations and individuals. CCT is a game-changer for every sector since it enables businesses of all stripes to forego asset ownership in favor of paying variable subscription fees. The assessment of the CCT provider selection (PS) issue necessitates the identification of a set of appropriate selection requirements and their sub-criteria. Another goal is to utilize this suggested method in a real-world case study after presenting a robust integrated system for assessing and choosing the best CCT supplier. There are a variety of qualitative and quantitative characteristics that might be at odds with one another or even unclear in CCT PS, making it a challenging multi-criteria selection issue. The single valued neutrosophic set is used to overcome the vague data. The neutrosophic TOPSIS method is used to give weights of criteria and rank the alternatives. The application is introduced by the results of the neutrosophic TOPSIS method.
Single Valued Neutrosophic Sets , Cloud Computing , MCDM , Ranking
[1] G. Büyüközkan, F. Göçer, and O. Feyzioğlu, “Cloud computing technology selection based on interval-valued intuitionistic fuzzy MCDM methods,” Soft Comput., vol. 22, no. 15, pp. 5091–5114, 2018.
[2] R. R. Kumar, B. Kumari, and C. Kumar, “CCS-OSSR: a framework based on hybrid MCDM for optimal service selection and ranking of cloud computing services,” Cluster Comput., vol. 24, no. 2, pp. 867–883, 2021.
[3] B. Lee, K. H. Oh, H. J. Park, U. M. Kim, and H. Y. Youn, “Resource reallocation of virtual machine in cloud computing with MCDM algorithm,” in 2014 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, IEEE, 2014, pp. 470–477.
[4] F. Nawaz, M. R. Asadabadi, N. K. Janjua, O. K. Hussain, E. Chang, and M. Saberi, “An MCDM method for cloud service selection using a Markov chain and the best-worst method,” Knowledge-Based Syst., vol. 159, pp. 120–131, 2018.
[5] A. M. Mostafa, “An MCDM approach for cloud computing service selection based on best-only method,” IEEE Access, vol. 9, pp. 155072–155086, 2021.
[6] P. Priyadarshinee, M. K. Jha, R. D. Raut, M. G. Kharat, and S. S. Kamble, “To identify the critical success factors for cloud computing adoption by MCDM technique,” Int. J. Bus. Inf. Syst., vol. 24, no. 4, pp. 469–510, 2017.
[7] J. Gyani, A. Ahmed, and M. A. Haq, “MCDM and various prioritization methods in AHP for CSS: A comprehensive review,” IEEE Access, 2022.
[8] Z. ur Rehman, O. K. Hussain, and F. K. Hussain, “Iaas cloud selection using MCDM methods,” in 2012 IEEE Ninth international conference on e-business engineering, IEEE, 2012, pp. 246–251.
[9] I. Irvanizam, Z. Zulfan, P. F. Nasir, M. Marzuki, S. Rusdiana, and N. Salwa, “An extended MULTIMOORA based on trapezoidal fuzzy neutrosophic sets and objective weighting method in group decision-making,” IEEE Access, vol. 10, pp. 47476–47498, 2022.
[10] S. Karadayi-Usta, “A novel neutrosophic set based hierarchical challenge analysis approach for servicizing business models: A case study of car share service network,” Comput. Ind. Eng., vol. 163, p. 107795, 2022.
[11] A. Z. Abualkrishik and S. N. A. Al, “The regulation and influence of physical exercise on human body’s neutrosophic set, respiratory system and nervous system,” Int. J. Neutrosophic Sci., vol. 18, no. 3, pp. 111–124, 2022.
[12] S. Broumi, M. Dhar, A. Bakhouyi, A. Bakali, and M. Talea, “Medical diagnosis problems based on neutrosophic sets and their hybrid structures: A survey,” Neutrosophic Sets Syst., vol. 49, no. 1, pp. 1–18, 2022.
[13] Irina V. Pustokhina,Denis A. Pustokhin, An Intelligent Neutrosophic Model for Evaluation Sustainable Housing Affordability, International Journal of Advances in Applied Computational Intelligence, Vol. 2 , No. 2 , (2022) : 45-53 (Doi : https://doi.org/10.54216/IJAACI.020205)
[14] S. Yalçın and I. Kaya, “Design and analysis of process capability indices Cpm and Cpmk by neutrosophic sets,” Iran. J. Fuzzy Syst., vol. 19, no. 1, pp. 13–30, 2022.
[15] Taif Khalid Shakir,Ahmed N. Al Masri, Single Valued Neutrosophic Set for Selection of Water Supply in Intelligent Farming, International Journal of Advances in Applied Computational Intelligence, Vol. 2 , No. 2, (2022) : 37-44 (Doi : https://doi.org/10.54216/IJAACI.020204)
[16] S. Said, M. M. Ibrahim, and M. M. Ismail, “An Integrated Multi-Criteria Decision-Making Approach for Identification and Ranking Solar Drying Barriers under Single-Valued Triangular Neutrosophic Sets (SVTNSs),” Neutrosophic Information Fusion, vol. 2, no. 1, p. 35, 2023.
[17] P. K. Singh, “Uncertainty analysis in document publications using single-valued neutrosophic set and collaborative entropy,” Artif. Intell. Rev., vol. 56, no. 3, pp. 2785–2809, 2023.
[18] M. Mohamed and N. El Saber, “Prioritization Thermochemical Materials based on Neutrosophic sets Hybrid MULTIMOORA Ranker Method,” Neutrosophic Information Fusion, vol. 2, no. 1, p. 8, 2023.
[19] S. Pramanik, “Single-Valued Neutrosophic Set: An Overview,” Transdisciplinarity, pp. 563–608, 2022.
[20] A. M. Ali, “A Multi-Criteria Decision-Making Approach for Piston Material Selection under Single-Valued Trapezoidal Neutrosophic Sets,” Neutrosophic Information Fusion, vol. 2, no. 1, p. 23, 2023.
[21] R. Garg, “MCDM-based parametric selection of cloud deployment models for an academic organization,” IEEE Trans. Cloud Comput., vol. 10, no. 2, pp. 863–871, 2020.
[22] C.-Y. Huang, P.-C. Hsu, and G.-H. Tzeng, “Evaluating cloud computing based telecommunications service quality enhancement by using a new hybrid MCDM model,” in Intelligent Decision Technologies: Proceedings of the 4th International Conference on Intelligent Decision Technologies (IDT´ 2012)-Volume 1, Springer, 2012, pp. 519–536.
[23] Neeraj, M. S. Goraya, and D. Singh, “A comparative analysis of prominently used MCDM methods in cloud environment,” J. Supercomput., vol. 77, pp. 3422–3449, 2021.
[24] R. R. Kumar, S. Mishra, and C. Kumar, “A novel framework for cloud service evaluation and selection using hybrid MCDM methods,” Arab. J. Sci. Eng., vol. 43, pp. 7015–7030, 2018.
[25] M. Ribas, A. S. Lima, N. Souza, A. Moura, F. R. C. Sousa, and G. Fenner, “Assessing cloud computing SaaS adoption for enterprise applications using a Petri net MCDM framework,” in 2014 IEEE Network Operations and Management Symposium (NOMS), IEEE, 2014, pp. 1–6.
[26] H. Ben Alla, S. Ben Alla, and A. Ezzati, “A priority based task scheduling in cloud computing using a hybrid MCDM model,” in Ubiquitous Networking: Third International Symposium, UNet 2017, Casablanca, Morocco, May 9-12, 2017, Revised Selected Papers 3, Springer, 2017, pp. 235–246.
[27] J. (Jong H. Park and H.-Y. Jeong, “The QoS-based MCDM system for SaaS ERP applications with Social Network,” J. Supercomput., vol. 66, pp. 614–632, 2013.
[28] Ahmed Abdelaziz,Alia N. Mahmoud Nova, Car Sharing Station Choice by using Interval Valued Neutrosophic WASPAS Method, International Journal of Advances in Applied Computational Intelligence, Vol. 2 , No. 2 , (2022) : 27-36 (Doi : https://doi.org/10.54216/IJAACI.020203)
[29] A. E. Youssef, “An integrated MCDM approach for cloud service selection based on TOPSIS and BWM,” IEEE Access, vol. 8, pp. 71851–71865, 2020.
[30] R. K. Tiwari and R. Kumar, “A robust and efficient MCDM-based framework for cloud service selection using modified TOPSIS,” Int. J. Cloud Appl. Comput., vol. 11, no. 1, pp. 21–51, 2021.
[31] S. C. Nayak, S. Parida, C. Tripathy, B. Pati, and C. R. Panigrahi, “Multicriteria decision‐making techniques for avoiding similar task scheduling conflict in cloud computing,” Int. J. Commun. Syst., vol. 33, no. 13, p. e4126, 2020.
[32] R. R. Kumar, S. Mishra, and C. Kumar, “Prioritizing the solution of cloud service selection using integrated MCDM methods under Fuzzy environment,” J. Supercomput., vol. 73, pp. 4652–4682, 2017.
