Abstract
Quality of service (QoS) is a concept that has been widely explored over the last decade to characterize Web Services (WS) from a non-functional perspective. However, QoS of WS is dynamic, different for each user, and complex by its nature; therefore, it is challenging to determine. Nevertheless, various authors have proposed different approaches for QoS finding. However, there is a gap in knowing how different QoS determining approaches affect QoS value and what is the relationship between the obtained QoS values. This paper presents the QoS value finding and analysis approach, which allows us to investigate the relationship between the applied methods and evaluate the correlation of results. The experiments were conducted by applying a fuzzy control system (FCS) and multi-criteria decision-making methods TOPSIS and WASPAS to determine QoS values and find the relationship between the obtained QoS values. The obtained results show that there is a strong positive linear relationship between QoS values obtained by WASPAS and TOPSIS, WASPAS and FCS, a very strong positive linear relationship between TOPSIS and FCS, and a very strong monotonic relationship between WASPAS and TOPSIS, WASPAS and FCS, and TOPSIS and FCS. Consequently, the three analysed QoS determining approaches can be successfully applied as alternatives.
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References
Ghafouri, H., Hashemi, M., Hung, P.C.K.: A survey on web service QoS prediction methods. IEEE Trans. Serv. Comput. (2020). https://doi.org/10.1109/TSC.2020.2980793n
Masdari, M., Khezri, H.: Service selection using fuzzy multi-criteria decision making: a comprehensive review. J. Ambient. Intell. Humaniz. Comput. 12(2), 2803–2834 (2020). https://doi.org/10.1007/s12652-020-02441-w
Hosseinzadeh, M., Hama, H.K., Ghafour, M.Y., Masdari, M., Ahmed, O.H., Khezri, H.: Service selection using multi-criteria decision making: a comprehensive overview. J. Netw. Syst. Manage. 28(4), 1639–1693 (2020). https://doi.org/10.1007/s10922-020-09553-w
Miliauskaitė, J.: Some methodological issues related to preliminary QoS planning in enterprise systems. Balt. J. Mod. Comput. 3, 149–163 (2015)
Yaghoubi, M., Maroosi, A.: Simulation and modeling of an improved multi-verse optimization algorithm for QoS-aware web service composition with service level agreements in the cloud environments. Simul. Model. Pract. Theory. 103, 102090 (2020). https://doi.org/10.1016/j.simpat.2020.102090
Chen, L., Ha, W.: Reliability prediction and QoS selection for web service composition. Int. J. Comput. Sci. Eng. 16, 202–211 (2018). https://doi.org/10.1504/IJCSE.2018.090442
Chattopadhyay, S., Banerjee, A.: QoS-aware automatic web service composition with multiple objectives. ACM Trans. Web. 14, 1–38 (2020). https://doi.org/10.1145/3389147
Adarme, M., Jimeno, M.: Qos-based pattern recognition approach for web service discovery: Ar_wsds. Appl. Sci. 11, 8092 (2021). https://doi.org/10.3390/app11178092
Chang, Z., Ding, D., Xia, Y.: A graph-based QoS prediction approach for web service recommendation. Appl. Intell. 51(10), 6728–6742 (2021). https://doi.org/10.1007/s10489-020-02120-5
Dang, D., Chen, C., Li, H., Yan, R., Guo, Z., Wang, X.: Deep knowledge-aware framework for web service recommendation. J. Supercomput. 77(12), 14280–14304 (2021). https://doi.org/10.1007/s11227-021-03832-2
Xiong, R., Wang, J., Zhang, N., Ma, Y.: Deep hybrid collaborative filtering for Web service recommendation. Expert Syst. Appl. 110, 191–205 (2018). https://doi.org/10.1016/j.eswa.2018.05.039
Hasan, M.H., Jaafar, J., Watada, J., Hassan, M.F., Aziz, I.A.: An interval type-2 fuzzy model of compliance monitoring for quality of web service. Ann. Oper. Res. 300(2), 415–441 (2019). https://doi.org/10.1007/s10479-019-03328-6
Ghafouri, S.H., Hashemi, S.M., Razzazi, M.R., Movaghar, A.: Web service quality of service prediction via regional reputation-based matrix factorization. Concurr. Comput. Pract. Exp. 33 (2021). https://doi.org/10.1002/cpe.6318
Wang, M., Liu, B., Li, J., Li, Y., Chen, H., Zhou, Z., Zhang, W.: A location-based approach for web service QoS prediction via multivariate time series forecast. In: Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS, pp. 36–39 (2020). https://doi.org/10.1109/ICSESS49938.2020.9237713
Kalibatiene, D., Miliauskaitė, J.: A hybrid systematic review approach on complexity issues in data-driven fuzzy inference systems development. Informatics 32, 85–118 (2021). https://doi.org/10.15388/21-INFOR444
Takagi, T., Sugeno, M.: Fuzzy Identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cybern. SMC-15, 116–132 (1985). https://doi.org/10.1109/TSMC.1985.6313399
Jatoth, C., Gangadharan, G.R., Fiore, U., Buyya, R.: SELCLOUD: a hybrid multi-criteria decision-making model for selection of cloud services. Soft. Comput. 23(13), 4701–4715 (2018). https://doi.org/10.1007/s00500-018-3120-2
Wang, S., Sun, Q., Zou, H., Yang, F.: Particle swarm optimization with Skyline operator for fast cloud-based web service composition. Mob. Netw. Appl. 18, 116–121 (2013). https://doi.org/10.1007/s11036-012-0373-3
Xu, J., et al.: Towards fuzzy QoS driven service selection with user requirements. In: Proceedings of 2017 International Conference on Progress in Informatics and Computing, PIC 2017. pp. 230–234 (2017). https://doi.org/10.1109/PIC.2017.8359548
Ghobaei-Arani, M., Souri, A.: LP-WSC: a linear programming approach for web service composition in geographically distributed cloud environments. J. Supercomput. 75(5), 2603–2628 (2018). https://doi.org/10.1007/s11227-018-2656-3
Keeney, R.L., Raiffa, H.: Decisions with Multiple Objectives. John Wiley & Sons, New York (1976)
Triantaphyllou, E.: Multi-criteria Decision Making Methods. Springer, Boston (2000). https://doi.org/10.1007/978-1-4757-3157-6_2
Kumar, A., et al.: A review of multi criteria decision making (MCDM) towards sustainable renewable energy development. Renew. Sustain. Energy Rev. 69, 596-609 (2017). https://doi.org/10.1016/j.rser.2016.11.191
Yazdani, M., Graeml, F.R.: VIKOR and its applications. Int. J. Strateg. Decis. Sci. 5, 56–83 (2014). https://doi.org/10.4018/ijsds.2014040105
Hwang, C.-L., Yoon, K.: Methods for Multiple Attribute Decision Making. Springer, Berlin (1981). https://doi.org/10.1007/978-3-642-48318-9_3
Triantaphyllou, E.: Multi-criteria Decision Making Methods: A Comparative Study. Springer, Boston (2000). https://doi.org/10.1007/978-1-4757-3157-6
Vafaei, N., Ribeiro, R.A., Camarinha-Matos, L.M.: Data normalisation techniques in decision making: case study with TOPSIS method. Int. J. Inf. Decis. Sci. 10(1), 19 (2018). https://doi.org/10.1504/IJIDS.2018.090667
Ghorabaee, M.K., Zavadskas, E.K., Olfat, L., Turskis, Z.: Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS). Informatics 26, 435–451 (2015). https://doi.org/10.15388/Informatica.2015.57
Roy, J., Sharma, H.K., Kar, S., Zavadskas, E.K., Saparauskas, J.: An extended COPRAS model for multi-criteria decision-making problems and its application in web-based hotel evaluation and selection. Econ. Res. Istraz. 32, 219–253 (2019). https://doi.org/10.1080/1331677X.2018.1543054
Alinezhad, A., Khalili, J.: COPRAS method. In: New Methods and Applications in Multiple Attribute Decision Making (MADM). ISORMS, vol. 277, pp. 87–91. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-15009-9_12
Bottani, E., Rizzi, A.: A fuzzy TOPSIS methodology to support outsourcing of logistics services. Supply Chain Manag. 11, 294–308 (2006). https://doi.org/10.1108/13598540610671743
Nădăban, S., Dzitac, S., Dzitac, I.: Fuzzy TOPSIS: a general view. Procedia Comput. Sci. 91, 823–831 (2016)
Junior, F.R.L., Hsiao, M.: A hesitant fuzzy topsis model to supplier performance evaluation. DYNA 88, 126–135 (2021). https://doi.org/10.15446/DYNA.V88N216.88320
Feng, Y., et al.: A novel hybrid fuzzy grey TOPSIS method: supplier evaluation of a collaborative manufacturing enterprise. Appl. Sci. 9 (2019). https://doi.org/10.3390/app9183770
Chakraborty, S., Zavadskas, E.K., Antucheviciene, J.: Applications of WASPAS method as a multi-criteria decision-making tool. Econ. Comput. Econ. Cybern. Stud. Res. 49, 5–22 (2015)
Yazdani, M., Zarate, P., Kazimieras Zavadskas, E., Turskis, Z.: A combined compromise solution (CoCoSo) method for multi-criteria decision-making problems. Manag. Decis. 57, 2501–2519 (2019). https://doi.org/10.1108/MD-05-2017-0458
Pandharbale, P.B., Mohanty, S.N., Jagadev, A.K.: Recent web service recommendation methods: a review. Mater. Today Proc. (2021). https://doi.org/10.1016/j.matpr.2021.01.783
Polska, O., Kudermetov, R., Shkarupylo, V.: The approach for QoS based web service selection with user’s preferences. Probl. Model. Des. Autom. 19–27 (2020). https://doi.org/10.31474/2074-7888-2020-2-19-27
Polska, O., Kudermetov, R., Shkarupylo, V.: Model of web services quality criteria hierarchy. Visnyk Zaporizhzhya Natl. Univ. Phys. Math. Sci. 2, 43–51 (2021). https://doi.org/10.26661/2413-6549-2020-2-06
Miliauskaitė, J.: The membership function construction in view-based framework. In: Haav, H.-M., Kalja, A., Robal, T. (eds.) 11th International Baltic Conference on Database and Information Systems (Baltic DB&IS 2014), pp. 125–132. Tallinn University of Technology Press, Tallinn (2014)
Mamdani, E.H.: Application of fuzzy algorithms for control of simple dynamic plant. Proc. Inst. Electr. Eng. 121, 1585–1588 (1974). https://doi.org/10.1049/piee.1974.0328
Zavadskas, E.K., Turskis, Z., Antucheviciene, J., Zakarevicius, A.: Optimization of weighted aggregated sum product assessment. Elektron. ir Elektrotechnika. 122, 3–6 (2012). https://doi.org/10.5755/j01.eee.122.6.1810
Hwang, C.-L., Yoon, K.: Methods for multiple attribute decision making. In: Multiple Attribute Decision Making. LNEMS, vol. 186. pp. 58–191. Springer, Heidelberg (1981). https://doi.org/10.1007/978-3-642-48318-9_3
Cleff, T.: Exploratory Data Analysis in Business and Economics. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-01517-0
Al-Masri, E.: QWS Dataset. https://qwsdata.github.io/. Accessed 24 Feb 2022
Al-Masri, E., Mahmoud, Q.H.: Investigating web services on the world wide web. In: Proceeding of the 17th International Conference on World Wide Web 2008, WWW 2008, pp. 795–804 (2008). https://doi.org/10.1145/1367497.1367605
Daoud, J.I.: Multicollinearity and regression analysis. IOP Conf. Ser. J. Phys. 949(1), 1–6. (2017). https://doi.org/10.1088/1742-6596/949/1/012009
Ouadah, A., Benouaret, K., Hadjali, A., Nader, F.: SkyAP-S3: a hybrid approach for efficient skyline services selection. In: Proceedings - 2015 IEEE 8th International Conference on Service-Oriented Computing and Applications, SOCA 2015, pp. 18–25. IEEE (2016). https://doi.org/10.1109/SOCA.2015.22
Sun, L., Ma, J., Zhang, Y., Dong, H., Hussain, F.K.: Cloud-FuSeR: fuzzy ontology and MCDM based cloud service selection. Futur. Gener. Comput. Syst. 57, 42–55 (2016). https://doi.org/10.1016/j.future.2015.11.025
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Kalibatienė, D., Miliauskaitė, J. (2022). On Web Service Quality Using Multi-criteria Decision-Making and Fuzzy Inference Methods. In: Ivanovic, M., Kirikova, M., Niedrite, L. (eds) Digital Business and Intelligent Systems. Baltic DB&IS 2022. Communications in Computer and Information Science, vol 1598. Springer, Cham. https://doi.org/10.1007/978-3-031-09850-5_3
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