Abstract
Compliance monitoring for quality of web service (QoWS) has accuracy issues due to uncertain network behaviors. Existing models use precise computation-based methods for defining and monitoring QoWS requirements, but these methods have limited ability to handle uncertainties. Consequently, the accuracy of the monitoring results is degraded. Defining expected QoWS using exact values is unrealistic, as generally not all service requestors know what values should be specified in the contract. Therefore, this paper proposes an interval type-2 (IT2) fuzzy model for QoWS compliance monitoring because it has greater capability than precise computation methods to reduce the effects of uncertainties. IT2 also has greater capability than the traditional fuzzy sets to manage uncertainty problem due to its non-crisp membership degrees assigned to the input. The model is able to perform compliance monitoring on linguistically defined QoWS. The model is developed based on fuzzy C-means algorithm, and the number of clusters is optimized using a clustering validity index. The model is constructed based on a Mamdani fuzzy inference system. The results show that the IT2 model outperforms type-1 fuzzy and precise computation-based models in terms of the accuracy of monitoring results. This research results in more accurate and precise QoWS compliance monitoring. It also provides user-centric QoWS specifications because requestors can define their requirements using linguistic values.

























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References
Allenotor, D., & Thulasiram, RK. (2008). A fuzzy grid-QoS framework for obtaining higher grid resources availability. In Proceedings of the 3rd International Conference on Advances in Grid and Pervasive Computing, Kunming, China, vol. 1788772 (pp. 128–139). Springer.
Baykasoglu, A., Golcuk, I., & Akyol, D. E. (2017). A fuzzy multiple-attribute decision making model to evaluate new product pricing strategies. Annals of Operations Research, 251(1–2), 205–242.
Benouaret, K., Benslimane, D., Hadjali, A., Barhamgi, M., Maamar, Z., & Sheng, Q. Z. (2014). Web service compositions with fuzzy preferences: A graded dominance relationship based approach. ACM Transactions on Internet Technology, 13(4), 1–34.
Berry, M. J. A., & Linoff, G. (1996). Data mining techniques for marketing, sales and customer support. New York: Wiley.
Bezdek, J. C., Ehrlich, R., & Full, W. (1984). FCM: The fuzzy c-means clustering algorithm. Computers and Geosciences, 10(2–3), 191–203.
Boumella, N., & Djouani, K. A. (2010). Type-2 fuzzy logic decision system for call admission control in next generation mobile networks. In 2010 IEEE Global Telecommunications Conference (GLOBECOM 2010).
Castillo, O., & Melin, P. (2008). Design of intelligent systems with interval type-2 fuzzy logic. In Type-2 Fuzzy Logic: Theory and Applications - Studies in Fuzziness and Soft Computing, vol. 223 (pp. 53–76). Springer.
Chen, P., & Dong, T-l. (2003). A fuzzy genetic algorithm for QoS multicast routing. Journal of Computer Communications, 266, 506–512.
Chhetri, M. B., Vo, Q. B., & Kowalczyk, R. (2013). AutoSLAM-A policy-based framework for automated SLA establishment in cloud environments. Concurrency and Computation: Practice and Experience, 27(9), 2413–2442.
Choi, Y., Lee, H., & Irani, Z. (2018). Big data-driven fuzzy cognitive map for prioritising IT service procurement in the public sector. Annals of Operations Research, 270(1–2), 75–104.
Deng, S., Huang, L., & Xu, G. (2014). Social network-based service recommendation with trust enhancement. Expert Systems with Applications, 41, 8075–8084.
Dereli, T., Baykasoglu, A., Altun, K., Durmusoglu, A., & Türksen, I. B. (2010). Industrial applications of type-2 fuzzy sets and systems: A concise review. Computers in Industry, 62(2), 125–137.
Dutta, M., Bhowmik, S., & Giri, C. (2014). Fuzzy logic based implementation for forest fire detection using wireless sensor network. Advanced Computing, Networking and Informatics, 1(The series Smart Innovation, Systems and Technologies), 319–327.
El Masri, A., Sardouk, A., Khoukhi, L., Merghem-Boulahia, L., & Gaiti, D. (2014). Multimedia support in wireless mesh networks using interval type-2 fuzzy logic system. In 6th International Conference on New Technologies, Mobility and Security.
Georgieva, O., & Petrova-Antonova, D. (2014). QoS-Aware web service selection accounting for uncertain constraints. In 2014 40th EUROMICRO Conference on Software Engineering and Advanced Applications (SEAA) (pp. 174–177).
Ghosh, S., & Dubey, S. K. (2013). Comparative analysis of K-means and fuzzy C-means algorithms. International Journal of Advanced Computer Science and Applications, 4(4), 35–39.
Gładysz, B. (2017). Fuzzy-probabilistic PERT. Annals of Operations Research, 258(2), 437–452.
Guldemır, H., & Sengur, A. (2006). Comparison of clustering algorithms for analog modulation classification. Expert Systems with Applications, 30(4), 642–649.
Hagras, H. A. (2007). Type-2 FLCs: A new generation of fuzzy controllers. IEEE Computational Intelligence Magazine, 2(1), 30–43.
Halkidi, M., Batistakis, Y., & Vazirgiannis, M. (2001). On clustering validation techniques. Journal of Intelligent Information Systems, 17(2–3), 107–145.
Han, S., & Mendel, J. M. (2012). A new method for managing the uncertainties in evaluating Multi-person Multi-criteria location choices, using a perceptual computer. Annals of Operations Research, 195(1), 277–309.
Jafelice, R. M., Bertone, A. M., & Bassanezi, R. C. (2015). A study on subjectivities of type 1 and 2 in parameters of differential equations. Tendencias em Matematica Aplicada e Computacional, 16(1), 51–60.
Jakubczyk, M., & Kaminski, B. (2017). Fuzzy approach to decision analysis with multiple criteria and uncertainty in health technology assessment. Annals of Operations Research, 251(1–2), 301–324.
Jindal, A., & Sangwan, K. S. (2017). Multi-objective fuzzy mathematical modelling of closed-loop supply chain considering economical and environmental factors. Annals of Operations Research, 257(1–2), 95–120.
Karnik, N. N., Mandel, J. M., & Liang, Q. (1999). Type-2 fuzzy logic systems. EEE Transactions on Fuzzy Systems, 7(6), 643–658.
Karnik, N. N., & Mendel, J. M. (2001). Centroid of a type-2 fuzzy set. Information Sciences, 132(1–4), 195–220.
Liu, X. (2013). A survey of continuous Karnik–Mendel algorithms and their generalizations. In A. Sadeghian et al. (Ed.) Advances in type-2 fuzzy sets and systems—studies in fuzziness and soft computing, vol. 301 (pp. 19–31).
Liu, J.-X., He, K.-Q., Wang, J., & Ning, D. A. (2011). Clustering method for web service discovery. In IEEE International Conference on Services Computing (pp. 729–730)
Li, K., Zhang, Y., Liu, W., & Gao, J. (2012). The application of fuzzy regression based on the trapezoidal fuzzy numbers to the software quality evaluation. Journal of Convergence Information Technology, 7(19), 293–300.
Martin, A., Lakshmi, T. M., & Venkatesan, V. P. (2014). An information delivery model for banking business. International Journal of Information Management: The Journal for Information Professionals archive, 34(2), 139–150.
Mendel, J. M. (2001). Uncertain rule-based fuzzy logic systems: Introduction and new directions. Upper Saddle River: Prentice-Hall.
Miramontes, I., Carlos Guzman, J., & Melin, P. (2018). Optimal design of interval type-2 fuzzy heart rate level classification systems using the bird swarm algorithm. Algorithms, 11(12), 206.
Mobedpour, D., & Ding, C. (2011). User-centered design of a QoS-based web service selection system.1–11. Service Oriented Computing and Applications,. https://doi.org/10.1007/s11761-011-0091-x.
Modica, G. D., Tomarchio, O., & Vita, L. (2009). Dynamic SLAs management in service oriented environments. Journal of Systems and Software, 82(5), 759–771. https://doi.org/10.1016/j.jss.2008.11.010.
Moharrer, M., Tahayori, H., Livi, L., Sadeghian, A., & Rizzi, A. (2015). Interval type-2 fuzzy sets to model linguistic label perception in online services satisfaction. Software Computing, 19(5), 237–250.
Oriol, M., Franch, X., & Marco, J. (2015). Monitoring the service-based system lifecycle with SALMon. Expert Systems with Applications, 42(19), 6507–6521.
Palacios, M., Garcia-Fanjul, J., Tuya, J., & Spanoudakis, G. (2015). Coverage-based testing for service level agreements. IEEE Transactions on Services Computing, 8(2), 299–313.
Pal, N. R., & Bezdek, J. C. (1995). On cluster validity for the fuzzy c-means model. IEEE Transactions on Fuzzy Systems, 3(3), 370–379.
Pal, N. R., & Bezdek, J. C. (1997). Correction to “on cluster validity for the fuzzy c-means model”. IEEE Transactions on Fuzzy Systems, 5, 152–153.
Pangsub, P., & Lekcharoen, S., (2010). An adaptive type-2 fuzzy for control policing mechanism over high speed networks. In The 2010 ECTI International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology.
Prenesti, E., & Gosmaro, F. (2015). Trueness, precision and accuracy: A critical overview of the concepts as well as proposals for revision. Accreditation and Quality Assurance, 20(1), 33–40.
Priya, N. H., Priya, A. M. S., & Chandramathi, S. (2014). QoS based selection and composition of web services—a fuzzy approach. Journal of Computer Science, 10(5), 861–868.
Rezaee, M. R., Lelieveldt, B. P. F., & Reiber, J. H. C. (1998). A new cluster validity index for the fuzzy c-mean. Pattern Recognition Letters, 19(3–4), 237–246.
Rosario, S., Benveniste, A., Haar, S., & Jard, C. (2008). Probabilistic QoS and soft contracts for transaction-based web services orchestrations. IEEE Transactions on Services Computing, 1(4), 187–200. https://doi.org/10.1109/tsc.2008.17.
Sehgal, A., & Agrawal, R. (2014). Integrated network selection scheme for remote healthcare systems. In 2014 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT) (pp 790–796).
Sharaf, S., & Djemame, K. (2015). Enabling service-level agreement renegotiation through extending WS-Agreement specification. Service Oriented Computing and Applications, 9(2), 177–191.
Sherchan, W., Loke, S. W., & Krishnaswamy, S. (2006). A fuzzy model for reasoning about reputation in web services. In 2006 ACM Symposium on Applied Computing (pp. 1886–1892).
Shivappa, N., & Manvi, S. (2014a). QoS mapping from user to network requirements in WMSN: A fuzzy logic based approach. In 2014 IEEE International Advance Computing Conference (IACC) (pp. 137–142).
Shivappa, N., & Manvi, S. (2014b). QoS mapping from user to network requirements in WMSN: A fuzzy logic based approach. In 2014 IEEE International Advance Computing Conference (IACC).
Shukla, A. K., & Muhuri, P. K. (2019). Big-data clustering with interval type-2 fuzzy uncertainty modeling in gene expression datasets. Engineering Applications for Artificial Intelligence, 77, 268–282.
Sundarraj, R. P. (2002). A model for standardizing human decisions concerning service-contracts management. Annals of Operations Research, 143(1), 171–189.
Tang, Y., Sun, F., & Sun, Z. (2005). Improved validation index for fuzzy clustering. In American Control Conference (pp. 1120–1125)
Tang, M., Dai, X., Liu, J., & Chen, J. (2016). Towards a trust evaluation middleware for cloud service selection. Future Generation Computer Systems, 74, 302–312.
Tay, K. M., & Lim, C. P. (2011). Optimization of Gaussian fuzzy membership functions and evaluation of the monotonicity property of fuzzy inference systems. In 2011 IEEE International Conference on Fuzzy Systems.
Teixeira, M., Ribeiro, R., Oliveira, C., & Massa, R. (2015). A quality-driven approach for resources planning in service-oriented architectures. Expert Systems with Applications, 42(12), 5366–5379.
Wahab, A., & Soomro, T. R. (2015). Implemetation of service oriented architecture using ITIL best practices. Journal of Engineering Science and Technology, 10(6), 765–770.
Wang, Y., & Liao, J. C. (2009). Why or why not service oriented architecture. In IITA International Conference on Services Science, Management and Engineering (pp. 65–67)
Wang, S., Liu, Z., Sun, Q., Zou, H., & Yang, F. (2014). Towards an accurate evaluation of quality of cloud service in service-oriented cloud computing. Journal of Intelligent Manufacturing, 25(2), 283–291.
Wang, L., & Wang, J. (2012). Feature weighting fuzzy clustering integrating rough sets and shadowed sets. International Journal of Pattern Recognition and Artificial Intelligence, 26(4), 1250010.
Wang, W., & Zhang, Y. (2007). On fuzzy cluster validity indices. Fuzzy Sets and Systems, 158(19), 2095–2117.
Wilrich, P.-T. (2007). Robust estimates of the theoretical standard deviation to be used in interlaboratory precision experiments. Accreditation and Quality Assurance, 12(5), 231–240.
Wu, D. (2012). An overview of alternative type-reduction approaches for reducing the computational cost of interval type-2 fuzzy logic controllers. In IEEE World Congress on Computational Intelligence.
Wu, D., & Mendel, J. M. (2007). Uncertainty measures for interval type-2 fuzzy sets. Information Sciences, 177(23), 5378–5393.
Wu, D., & Tan, W. W. (2006). Genetic learning and performance evaluation of interval type-2 fuzzy logic controllers. Engineering Applications of Artificial Intelligence, 19(8), 829–841.
Wu, K.-L., & Yang, M.-S. (2005). A cluster validity index for fuzzy clustering. Pattern Recognition Letters, 26(9), 1275–1291.
Xie, X., & Beni, G. (1991). Validity measure for fuzzy clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 3(8), 841–847.
Yan, Y., & Chen, M. (2015). Anytime QoS-aware service composition over the GraphPlan. Service Oriented Computing and Applications, 9(1), 1–19.
Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning-1. Information Sciences, 8(3), 199–249.
Zadeh, L. A. (2008). Is there a need for fuzzy logic? Information Sciences, 178, 2751–2779.
Zemni, M. A., Benbernou, S., & Carro, M. (2010). A soft constraint-based approach to QoS-Aware service selection. Service-Oriented Computing—Lecture Notes in Computer Science, 6470, 596–602.
Zhang, H. X., Zhang, B., & Wang, F. (2009). Automatic fuzzy rules generation using fuzzy genetic algorithm. In 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery.
Zhang, L., Zou, H., & Yang, F. (2011). A dynamic web service composition algorithm based on TOPSIS. Journal of Networks, 6(9), 1296–1304.
Zhao, T., Li, P., & Cao, J. (2019). Soft sensor modeling of chemical process based on self-organizing recurrent interval type-2 fuzzy neural network. ISA Transactions, 84, 237–246.
Zhao, L., Sakr, S., & Liu, A. (2015). A framework for consumer-centric SLA management of cloud-hosted databases. IEEE Transactions on Services Computing, 8(4), 534–549.
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This research is an ongoing research supported by Fundamental Research Grant Scheme (FRGS/1/2018/ICT02/UTP/02/1); a Grant funded by the Ministry of Education, Malaysia.
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Hasan, M.H., Jaafar, J., Watada, J. et al. An interval type-2 fuzzy model of compliance monitoring for quality of web service. Ann Oper Res 300, 415–441 (2021). https://doi.org/10.1007/s10479-019-03328-6
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DOI: https://doi.org/10.1007/s10479-019-03328-6