1
Department of Management, Applied College, Jazan University, Jazan, KSA
(abSalamai@jazanu.edu.sa)
Abstract :
The variety of cloud-based services that are now accessible is expanding at a fast pace, and as a result, it has become much more difficult for regular customers to choose the appropriate cloud services. When selecting an appropriate service to use in a setting where there is a high degree of unpredictability, it is in the user's best interest to be able to deal with ambiguous information. This is because the Cloud service environment contains a great number of unknowns, which may prevent the user from making wise choices. In this paper, the authors propose a framework that they call the Optimal Service Choice and Priority of Cloud Computing (CC) Service. This methodology gives cloud customers the ability to evaluate the various service options available to them relying on QoS (Quality of Requirements) standards. The model employs a mixed approach to decision making that is based on many factors. The PROMETHEE is used for the purpose of ranking and prioritizing the QoS criteria, and to get the final rank of cloud services. The suggested methodology is a Multi-criteria decision making approach due to this problem contain many conflicting criteria. The PROMETHEE is integrated with neutrosophic environment to overcome uncertainty information. The application of methodology is provided.
Keywords :
Cloud Service; MCDM; Neutrosophic Sets; Cloud Computing; Quality of Service
References :
[1] J. L. Schaefer, J. C. M. Siluk, and P. S. de Carvalho, “An MCDM-based approach to evaluate the performance objectives for strategic management and development of Energy Cloud,” Journal of Cleaner Production, vol. 320, p. 128853, 2021.
[2] R. R. Kumar, S. Mishra, and C. Kumar, “Prioritizing the solution of cloud service selection using integrated MCDM methods under Fuzzy environment,” The Journal of Supercomputing, vol. 73, pp. 4652–4682, 2017.
[3] 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, 2012, pp. 246–251.
[4] A. Al-Faifi, B. Song, M. M. Hassan, A. Alamri, and A. Gumaei, “A hybrid multi criteria decision method for cloud service selection from Smart data,” Future Generation Computer Systems, vol. 93, pp. 43–57, 2019.
[5] J. (Jong H. Park and H.-Y. Jeong, “The QoS-based MCDM system for SaaS ERP applications with Social Network,” The Journal of Supercomputing, vol. 66, pp. 614–632, 2013.
[6] G. Büyüközkan, F. Göçer, and O. Feyzioğlu, “Cloud computing technology selection based on interval-valued intuitionistic fuzzy MCDM methods,” Soft Computing, vol. 22, no. 15, pp. 5091–5114, 2018.
[7] R. R. Kumar and C. Kumar, “A multi criteria decision making method for cloud service selection and ranking,” International Journal of Ambient Computing and Intelligence (IJACI), vol. 9, no. 3, pp. 1–14, 2018.
[8] I. Petrov, “Combined criteria weighting in MCDM: AHP in blocks with traditional Entropy and novel Hierarchy in TOPSIS evaluation of Cloud Services,” in 2021 Big Data, Knowledge and Control Systems Engineering (BdKCSE), 2021, pp. 1–9.
[9] Z. ur Rehman, F. K. Hussain, and O. K. Hussain, “Towards multi-criteria cloud service selection,” in 2011 fifth international conference on innovative mobile and internet services in ubiquitous computing, 2011, pp. 44–48.
[10] T. Subramanian and N. Savarimuthu, “Cloud service evaluation and selection using fuzzy hybrid MCDM approach in marketplace,” International Journal of Fuzzy System Applications (IJFSA), vol. 5, no. 2, pp. 118–153, 2016.
[11] R. Garg, “MCDM-based parametric selection of cloud deployment models for an academic organization,” IEEE Transactions on Cloud Computing, vol. 10, no. 2, pp. 863–871, 2020.
[12] 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 Computing, vol. 24, no. 2, pp. 867–883, 2021.
[13] A. M. Mostafa, “An MCDM approach for cloud computing service selection based on best-only method,” IEEE Access, vol. 9, pp. 155072–155086, 2021.
[14] A. A. Salamai, “Neutrosophic-based machine learning context for the trustworthiness of devices in the internet of things,” Journal of Neutrosophic and Fuzzy Systems, vol. 1, no. 1, p. 59, 2023.
[15] M. S. Goraya and D. Singh, “A comparative analysis of prominently used MCDM methods in cloud environment,” The Journal of Supercomputing, vol. 77, pp. 3422–3449, 2021.
[16] J. Sidhu and S. Singh, “Design and comparative analysis of MCDM-based multi-dimensional trust evaluation schemes for determining trustworthiness of cloud service providers,” Journal of Grid Computing, vol. 15, pp. 197–218, 2017.
[17] R. K. Tiwari and R. Kumar, “A robust and efficient MCDM-based framework for cloud service selection using modified TOPSIS,” International Journal of Cloud Applications and Computing (IJCAC), vol. 11, no. 1, pp. 21–51, 2021.
[18] D. Xu, X. Wei, H. Ding, and H. Bin, “A new method based on PROMETHEE and TODIM for multi-attribute decision-making with single-valued neutrosophic sets,” Mathematics, vol. 8, no. 10, p. 1816, 2020.
[19] P. Liu, S. Cheng, and Y. Zhang, “An extended multi-criteria group decision-making PROMETHEE method based on probability multi-valued neutrosophic sets,” International journal of fuzzy systems, vol. 21, pp. 388–406, 2019.
[20] E. K. Zavadskas, R. Bausys, A. Kaklauskas, and S. Raslanas, “Hedonic shopping rent valuation by one-to-one neuromarketing and neutrosophic PROMETHEE method,” Applied Soft Computing, vol. 85, p. 105832, 2019.
[21] F. Altun, R. Şahin, and C. Güler, “Multi-criteria decision making approach based on PROMETHEE with probabilistic simplified neutrosophic sets,” Soft Computing, vol. 24, no. 7, pp. 4899–4915, 2020.
[22] N. A. Nabeeh, A. Abdel-Monem, and A. Abdelmouty, “A novel methodology for assessment of hospital service according to BWM, MABAC, PROMETHEE II,” Neutrosophic Sets and Systems, vol. 31, pp. 63–79, 2020.
[23] A. A. Salamai, “An integrated neutrosophic SWARA and VIKOR method for ranking risks of green supply chain,” Neutrosophic Sets and Systems, vol. 41, pp. 113–125, 2021.
[24] R. R. Kumar, S. Mishra, and C. Kumar, “A novel framework for cloud service evaluation and selection using hybrid MCDM methods,” Arabian Journal for Science and Engineering, vol. 43, pp. 7015–7030, 2018.
[25] 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 Systems, vol. 159, pp. 120–131, 2018.
[26] L. Sun, J. Ma, Y. Zhang, H. Dong, and F. K. Hussain, “Cloud-FuSeR: Fuzzy ontology and MCDM based cloud service selection,” Future Generation Computer Systems, vol. 57, pp. 42–55, 2016.
[27] A. E. Youssef, “An integrated MCDM approach for cloud service selection based on TOPSIS and BWM,” IEEE Access, vol. 8, pp. 71851–71865, 2020.
| Style | # |
|---|---|
| MLA | Abdullah Ali Salamai. "Evaluation and Selection of Cloud Service: A neutrosophic model." Neutrosophic and Information Fusion, Vol. 1, No. 2, 2023 ,PP. 16-25 (Doi : https://doi.org/10.54216/NIF.010202) |
| APA | Abdullah Ali Salamai. (2023). Evaluation and Selection of Cloud Service: A neutrosophic model. Journal of Neutrosophic and Information Fusion, 1 ( 2 ), 16-25 (Doi : https://doi.org/10.54216/NIF.010202) |
| Chicago | Abdullah Ali Salamai. "Evaluation and Selection of Cloud Service: A neutrosophic model." Journal of Neutrosophic and Information Fusion, 1 no. 2 (2023): 16-25 (Doi : https://doi.org/10.54216/NIF.010202) |
| Harvard | Abdullah Ali Salamai. (2023). Evaluation and Selection of Cloud Service: A neutrosophic model. Journal of Neutrosophic and Information Fusion, 1 ( 2 ), 16-25 (Doi : https://doi.org/10.54216/NIF.010202) |
| Vancouver | Abdullah Ali Salamai. Evaluation and Selection of Cloud Service: A neutrosophic model. Journal of Neutrosophic and Information Fusion, (2023); 1 ( 2 ): 16-25 (Doi : https://doi.org/10.54216/NIF.010202) |
| IEEE | Abdullah Ali Salamai, Evaluation and Selection of Cloud Service: A neutrosophic model, Journal of Neutrosophic and Information Fusion, Vol. 1 , No. 2 , (2023) : 16-25 (Doi : https://doi.org/10.54216/NIF.010202) |