International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

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 Volume 9, Issue 10 (October 2022), Pages: 149-165

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 Original Research Paper

 An integrated fuzzy-VIKOR-DEMATEL-TOPSIS technique for assessing QoS factors of SOA

 Author(s): Paul Aazagreyir 1, 2, *, Peter Appiahene 2, Obed Appiah 2, Samuel Boateng 2

 Affiliation(s):

 1Department of Information Technology Studies, University of Professional Studies, Accra, Ghana
 2Department of Computer Science and Informatics, University of Energy and Natural Resources, Sunyani, Ghana

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 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0002-9586-5781

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2022.10.018

 Abstract:

Quality of service (QoS) is a very important concept in service-oriented architecture (SOA). While there is a growing body of study on QoS-based service selection based on SOA, there is little research on analyzing QoS factors from the viewpoints of IT workers and researchers. As a result, the purpose of the current study aims to offer an integrated fuzzy VIKOR-TOPSIS-DEMATEL approach framework for evaluating QoS factors of online services from the viewpoint of experts in a fuzzy environment. A numerical assessment of the QoS factors for a case firm in Ghana indicated that the suggested technique is appropriate for the problem. Furthermore, the technique outcomes divided QoS factors into cause-effect variables, ranked QoS factors, and lastly, suggested conflicting QoS factors. The results from the Fuzzy DEMATEL aspect of the proposed approach found integrity, availability, accessibility, compliance, documentation, latency, and adaptability as causal variables. While response time, cost/price, reliability, performance, security, reputation, throughput, best practices, success ability, encryption, portability, storage, and consistency are regarded as influential variables. The Fuzzy TOPSIS aspect of the technique found adaptability, documentation, consistency, transaction, and accessibility are the most ranked QoS factors of online services. The fuzzy VIKOR side of the proposed method discovers integrity, cost, and latency as incommensurable QoS factors. Finally, a sensitivity analysis was carried out, and the results show the model is robust. This study confirms the position of existing knowledge on sensitivity analysis in the QoS literature. In the issue of QoS factor evaluation, this work effectively blended three MCDM techniques. The study's shortcoming stems from its reliance on data from QoS specialists from only one developing nation (i.e. Ghana).

 © 2022 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: QoS factors, Fuzzy TOPSIS, Fuzzy DEMATEL, Fuzzy VIKOR, Service

 Article History: Received 17 March 2022, Received in revised form 9 July 2022, Accepted 14 July 2022

 Acknowledgment 

No Acknowledgment.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Aazagreyir P, Appiahene P, and Appiah O et al. (2022). An integrated fuzzy-VIKOR-DEMATEL-TOPSIS technique for assessing QoS factors of SOA. International Journal of Advanced and Applied Sciences, 9(10): 149-165

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 Figures

 Fig. 1 Fig. 2 Fig. 3 Fig. 4

 Tables

 Table 1 Table 2 Table 3 Table 4 Table 5 Table 6 Table 7 Table 8 Table 9 Table 10 Table 11 Table 12 Table 13 Table 14 Table 15 Table 16

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 References (59)

  1. Afful-Dadzie E, Nabareseh S, and Oplatková ZK (2014a). Fuzzy VIKOR approach: Evaluating quality of internet health information. In the 2014 Federated Conference on Computer Science and Information Systems, IEEE, Warsaw, Poland: 183-190. https://doi.org/10.15439/2014F203   [Google Scholar]
  2. Afful-Dadzie E, Nabareseh S, Klímek P, and Oplatková ZK (2014b). Ranking fragile states for support facility: A fuzzy TOPSIS approach. In the 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), IEEE, Xiamen, China: 255-261. https://doi.org/10.1109/FSKD.2014.6980842   [Google Scholar]
  3. Al-Masri E and Mahmoud QH (2008). Investigating web services on the world wide web. In the 17th International Conference on World Wide Web, Association for Computing Machinery, Beijing, China: 795-804. https://doi.org/10.1145/1367497.1367605   [Google Scholar]
  4. Atthirawong W, Panprung W, and Leerojanaprapa K (2018). Using DEMATEL to explore the relationship of factors affecting consumers' behaviors in buying green products. In the 32nd European Conference on Modelling and Simulation (ECMS), Wilhelmshaven, Germany: 317-322. https://doi.org/10.7148/2018-0317   [Google Scholar]
  5. Ayouni S, Menzli LJ, Hajjej F, Maddeh M, and Al-Otaibi S (2021). Fuzzy VIKOR application for learning management systems evaluation in higher education. International Journal of Information and Communication Technology Education, 17(2): 17-35. https://doi.org/10.4018/IJICTE.2021040102   [Google Scholar]
  6. Balin A, Şener B, and Demirel H (2020). Application of fuzzy VIKOR method for the evaluation and selection of a suitable tugboat. Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment, 234(2): 502-509. https://doi.org/10.1177/1475090219875879   [Google Scholar]
  7. Beran PP, Vinek E, and Schikuta E (2013). An adaptive framework for QoS‐aware service selection optimization. International Journal of Web Information Systems, 9(1): 32-55. https://doi.org/10.1108/17440081311316370   [Google Scholar]
  8. Büyüközkan G and Çifçi G (2012). A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers. Expert Systems with Applications, 39(3): 3000-3011. https://doi.org/10.1016/j.eswa.2011.08.162   [Google Scholar]
  9. Chen Y, Jiang L, Zhang J, and Dong X (2016). A robust service selection method based on uncertain QoS. Mathematical Problems in Engineering, 2016: 9480769. https://doi.org/10.1155/2016/9480769   [Google Scholar]
  10. Chen Y, Yu J, and Khan S (2010). Spatial sensitivity analysis of multi-criteria weights in GIS-based land suitability evaluation. Environmental Modelling and Software, 25(12): 1582-1591. https://doi.org/10.1016/j.envsoft.2010.06.001   [Google Scholar]
  11. Choi CR and Jeong HY (2014). A broker-based quality evaluation system for service selection according to the QoS preferences of users. Information Sciences, 277: 553-566. https://doi.org/10.1016/j.ins.2014.02.141   [Google Scholar]
  12. Dalvi-Esfahani M, Niknafs A, Kuss DJ, Nilashi M, and Afrough S (2019). Social media addiction: Applying the DEMATEL approach. Telematics and Informatics, 43: 101250. https://doi.org/10.1016/j.tele.2019.101250   [Google Scholar]
  13. Evans JR (1984). Sensitivity analysis in decision theory. Decision Sciences, 15(2): 239-247. https://doi.org/10.1111/j.1540-5915.1984.tb01211.x   [Google Scholar]
  14. Gao ZP, Jian C, Qiu XS, and Meng LM (2009). QoE/QoS driven simulated annealing-based genetic algorithm for web services selection. The Journal of China Universities of Posts and Telecommunications, 16: 102-107. https://doi.org/10.1016/S1005-8885(08)60347-7   [Google Scholar]
  15. Gohar P and Purohit L (2015). Discovery and prioritization of web services based on fuzzy user preferences for QoS. In the 2015 International Conference on Computer, Communication and Control, IEEE, Indore, India: 1-6. https://doi.org/10.1109/IC4.2015.7375702   [Google Scholar] PMid:25830063 PMCid:PMC4372183
  16. Hemati F and Alroaia Y (2012). An application of DEMATEL technique to find the effect of different factors influencing outsourcing activities in water and switch organization. Management Science Letters, 2(7): 2305-2310. https://doi.org/10.5267/j.msl.2012.08.013   [Google Scholar]
  17. Jain A and Easo S (2012). An enhance approach for web services discovery with QoS. In the 9th International Conference on Wireless and Optical Communications Networks, IEEE, Indore, India: 1-3. https://doi.org/10.1109/WOCN.2012.6335533   [Google Scholar]
  18. Jassbi J, Mohamadnejad F, and Nasrollahzadeh H (2011). A fuzzy DEMATEL framework for modeling cause and effect relationships of strategy map. Expert Systems with Applications, 38(5): 5967-5973. https://doi.org/10.1016/j.eswa.2010.11.026   [Google Scholar]
  19. Jing S, Tang Y, and Yan J (2018). The application of fuzzy VIKOR for the design scheme selection in lean management. Mathematical Problems in Engineering, 2018: 9253643. https://doi.org/10.1155/2018/9253643   [Google Scholar]
  20. Kaviani MA, Yazdi AK, Ocampo L, and Kusi-Sarpong S (2019). An integrated grey-based multi-criteria decision-making approach for supplier evaluation and selection in the oil and gas industry. Kybernetes, 49(2): 406-441. https://doi.org/10.1108/K-05-2018-0265   [Google Scholar]
  21. Kumar RR and Kumar C (2016). An evaluation system for cloud service selection using fuzzy AHP. In the 11th International Conference on Industrial and Information Systems, IEEE, Roorkee, India: 821-826. https://doi.org/10.1109/ICIINFS.2016.8263052   [Google Scholar] PMCid:PMC5143805
  22. Kumar RR, Mishra S, and Kumar C (2018). A novel framework for cloud service evaluation and selection using hybrid MCDM methods. Arabian Journal for Science and Engineering, 43(12): 7015-7030. https://doi.org/10.1007/s13369-017-2975-3   [Google Scholar]
  23. Lin M, Huang C, and Xu Z (2019). TOPSIS method based on correlation coefficient and entropy measure for linguistic Pythagorean fuzzy sets and its application to multiple attribute decision making. Complexity, 2019: 6967390. https://doi.org/10.1155/2019/6967390   [Google Scholar]
  24. Lin SY, Lai CH, Wu CH, and Lo CC (2013). A trustworthy QoS-based mechanism for web service discovery based on collaborative filtering. In the 5th International Conference on Ubiquitous and Future Networks, IEEE, Da Nang, Vietnam: 71-76. https://doi.org/10.1109/ICUFN.2013.6614781   [Google Scholar]
  25. Liu B and Zhang Z (2017). QoS-aware service composition for cloud manufacturing based on the optimal construction of synergistic elementary service groups. The International Journal of Advanced Manufacturing Technology, 88(9): 2757-2771. https://doi.org/10.1007/s00170-016-8992-7   [Google Scholar]
  26. Liu Y, Ngu AH, and Zeng LZ (2004). QoS computation and policing in dynamic web service selection. In the 13th International World Wide Web Conference on Alternate Track Papers and Posters, Association for Computing Machinery, New York, USA: 66-73. https://doi.org/10.1145/1013367.1013379   [Google Scholar] PMCid:PMC420457
  27. Maheswari S and Karpagam GR (2018). Performance evaluation of semantic based service selection methods. Computers and Electrical Engineering, 71: 966-977. https://doi.org/10.1016/j.compeleceng.2017.10.006   [Google Scholar]
  28. Maroc S and Zhang JB (2020). Towards security effectiveness evaluation for cloud services selection following a risk-driven approach. International Journal of Advanced Computer Science and Applications, 11(1): 20-31. https://doi.org/10.14569/IJACSA.2020.0110103   [Google Scholar]
  29. Meksavang P, Shi H, Lin SM, and Liu HC (2019). An extended picture fuzzy VIKOR approach for sustainable supplier management and its application in the beef industry. Symmetry, 11(4): 468. https://doi.org/10.3390/sym11040468   [Google Scholar]
  30. Mousa A and Bentahar J (2016). An efficient QoS-aware web services selection using social spider algorithm. Procedia Computer Science, 94: 176-182. https://doi.org/10.1016/j.procs.2016.08.027   [Google Scholar]
  31. Opricovic S, Tzeng GH, and Engn FC (2004). Emerging research fronts-2009. European Journal of Operational Research, 156(2): 445-455. https://doi.org/10.1016/S0377-2217(03)00020-1   [Google Scholar]
  32. Ortiz-Barrios M, Cabarcas-Reyes J, Ishizaka A, Barbati M, Jaramillo-Rueda N, and de Jesús CZG (2021). A hybrid fuzzy multi-criteria decision making model for selecting a sustainable supplier of forklift filters: A case study from the mining industry. Annals of Operations Research, 307(1): 443-481. https://doi.org/10.1007/s10479-020-03737-y   [Google Scholar]
  33. Pandey M, Litoriya R, and Pandey P (2019). Application of fuzzy DEMATEL approach in analyzing mobile app issues. Programming and Computer Software, 45(5): 268-287. https://doi.org/10.1134/S0361768819050050   [Google Scholar]
  34. Park JJH and Jeong HY (2013). The QoS-based MCDM system for SaaS ERP applications with social network. The Journal of Supercomputing, 66(2): 614-632. https://doi.org/10.1007/s11227-012-0832-4   [Google Scholar]
  35. Pechová H (2015). Application of DEMATEL method in CRM performance measurement. In the Conference of MEKON Selected Papers: 17th International Conference for Young Researchers and Ph.D. Students, Ostrava, Czech Republic: 95-106.   [Google Scholar]
  36. Peleckis K (2021). Application of the DEMATEL model for assessing IT sector’s sustainability. Sustainability, 13(24): 13866. https://doi.org/10.3390/su132413866   [Google Scholar]
  37. Ran S (2003). A model for web services discovery with QoS. Association for Computing Machinery (ACM) Sigecom Exchanges, 4(1): 1-10. https://doi.org/10.1145/844357.844360   [Google Scholar]
  38. Rhimi F, Yahia SB, and Ahmed SB (2016). Refining the Skyline with fuzzy similariy measures and TOPSIS method for the optimization of web services composition. In the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, Vancouver, Canada: 2091-2097. https://doi.org/10.1109/FUZZ-IEEE.2016.7737949   [Google Scholar]
  39. Ruiz JZ and Rubira CM (2016). Quality of service conflict during web service monitoring: A case study. Electronic Notes in Theoretical Computer Science, 321: 113-127. https://doi.org/10.1016/j.entcs.2016.02.007   [Google Scholar]
  40. Safari H, Faghih A, and Fathi MR (2012). Fuzzy multi-criteria decision making method for facility location selection. African Journal of Business Management, 6(1): 206-212. https://doi.org/10.5897/AJBM11.1760   [Google Scholar]
  41. Sangaiah AK, Subramaniam PR, and Zheng X (2015). A combined fuzzy DEMATEL and fuzzy TOPSIS approach for evaluating GSD project outcome factors. Neural Computing and Applications, 26(5): 1025-1040. https://doi.org/10.1007/s00521-014-1771-1   [Google Scholar]
  42. Seghir F and Khababa G (2021). Fuzzy teaching learning based optimization approach for solving the QoS-aware web service selection problem in uncertain environments. Journal of Ambient Intelligence and Humanized Computing, 12(12): 10667-10697. https://doi.org/10.1007/s12652-020-02879-y   [Google Scholar]
  43. Seker S and Zavadskas EK (2017). Application of fuzzy DEMATEL method for analyzing occupational risks on construction sites. Sustainability, 9(11): 2083. https://doi.org/10.3390/su9112083   [Google Scholar]
  44. Seleem SN, Attia EA, Karam A, and El-Assal A (2020). A lean manufacturing road map using fuzzy-DEMATEL with case-based analysis. International Journal of Lean Six Sigma, 11(5): 903-928. https://doi.org/10.1108/IJLSS-12-2017-0147   [Google Scholar]
  45. Singh N and Tyagi K (2017). Ranking of services for reliability estimation of SOA system using fuzzy multicriteria analysis with similarity-based approach. International Journal of System Assurance Engineering and Management, 8(1): 317-326. https://doi.org/10.1007/s13198-015-0339-5   [Google Scholar]
  46. Soner O (2021). Application of fuzzy DEMATEL method for analysing of accidents in enclosed spaces onboard ships. Ocean Engineering, 220: 108507. https://doi.org/10.1016/j.oceaneng.2020.108507   [Google Scholar]
  47. Sridevi S, Karpagam GR, and Vinoth Kumar B (2021). Incorporating blockchain for semantic web service selection (SWSS) method. Sādhanā, 46: 89. https://doi.org/10.1007/s12046-021-01619-y   [Google Scholar]
  48. Sujith AVLN, Reddy ARM, and Madhavi K (2018). Evaluating the QoS cognizance in composition of cloud services: A systematic literature review. International Journal of Engineering and Technology, 7(4.6): 141-149. https://doi.org/10.14419/ijet.v7i4.6.20451   [Google Scholar]
  49. Tabrizi BH, Torabi SA, and Ghaderi SF (2016). A novel project portfolio selection framework: An application of fuzzy DEMATEL and multi-choice goal programming. Scientia Iranica, 23(6): 2945-2958. https://doi.org/10.24200/sci.2016.4004   [Google Scholar]
  50. Thasni T, Kalaiarasan C, and Venkatesh KA (2020). Cloud service provider selection using fuzzy TOPSIS. In the IEEE International Conference for Innovation in Technology, IEEE, Bangaluru, India: 1-5. https://doi.org/10.1109/INOCON50539.2020.9298207   [Google Scholar]
  51. Tiwari RK and Kumar R (2021). G-TOPSIS: A cloud service selection framework using Gaussian TOPSIS for rank reversal problem. The Journal of Supercomputing, 77(1): 523-562. https://doi.org/10.1007/s11227-020-03284-0   [Google Scholar]
  52. Tong E, Niu W, Tian Y, Liu J, Baker T, Verma S, and Liu Z (2021). A hierarchical energy-efficient service selection approach with QoS constraints for internet of things. IEEE Transactions on Green Communications and Networking, 5(2): 645-657. https://doi.org/10.1109/TGCN.2021.3069121   [Google Scholar]
  53. Ustaoglu E and Aydınoglu AC (2020). Suitability evaluation of urban construction land in Pendik district of Istanbul, Turkey. Land Use Policy, 99: 104783. https://doi.org/10.1016/j.landusepol.2020.104783   [Google Scholar]
  54. Vesyropoulos N and Georgiadis CK (2015). QoS‐Based filters in web service compositions: Utilizing multi‐criteria decision analysis methods. Journal of Multi‐Criteria Decision Analysis, 22(5-6): 279-292. https://doi.org/10.1002/mcda.1538   [Google Scholar]
  55. Wang HC, Chiu WP, and Wu SC (2015). QoS-driven selection of web service considering group preference. Computer Networks, 93(1): 111-124. https://doi.org/10.1016/j.comnet.2015.10.014   [Google Scholar]
  56. Wang T, Zhang P, Liu J, and Zhang M (2021). Many-objective cloud manufacturing service selection and scheduling with an evolutionary algorithm based on adaptive environment selection strategy. Applied Soft Computing, 112: 107737. https://doi.org/10.1016/j.asoc.2021.107737   [Google Scholar]
  57. Zhang G, Chen L, and Ha W (2012a). Service selection of ensuring transactional reliability and QoS for web service composition. Mathematical Problems in Engineering, 2012: 641361. https://doi.org/10.1155/2012/641361   [Google Scholar]
  58. Zhang LC, Li CJ, and Yu ZL (2012b). Dynamic web service selection group decision-making based on heterogeneous QoS models. The Journal of China Universities of Posts and Telecommunications, 19(3): 80-90. https://doi.org/10.1016/S1005-8885(11)60269-0   [Google Scholar]
  59. Zou H, Zhang L, Yang F, and Zhao Y (2010). A web service composition algorithmic method based on TOPSIS supporting multiple decision-makers. In the 6th World Congress on Services, IEEE, Miami, USA: 158-159. https://doi.org/10.1109/SERVICES.2010.110   [Google Scholar]