An interval type-2 fuzzy model of compliance monitoring for quality of web service

  • Mohd Hilmi HasanEmail author
  • Jafreezal Jaafar
  • Junzo Watada
  • Mohd Fadzil Hassan
  • Izzatdin Abdul Aziz
S.I.: Integrated Uncertainty in Knowledge Modelling & Decision Making 2018


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.


Web services monitoring Quality of web service QoWS monitoring Interval type-2 fuzzy Uncertainties 



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.


  1. 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.Google Scholar
  2. 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.CrossRefGoogle Scholar
  3. 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.CrossRefGoogle Scholar
  4. Berry, M. J. A., & Linoff, G. (1996). Data mining techniques for marketing, sales and customer support. New York: Wiley.Google Scholar
  5. Bezdek, J. C., Ehrlich, R., & Full, W. (1984). FCM: The fuzzy c-means clustering algorithm. Computers and Geosciences, 10(2–3), 191–203.CrossRefGoogle Scholar
  6. 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).Google Scholar
  7. 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.Google Scholar
  8. Chen, P., & Dong, T-l. (2003). A fuzzy genetic algorithm for QoS multicast routing. Journal of Computer Communications, 266, 506–512.CrossRefGoogle Scholar
  9. 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.CrossRefGoogle Scholar
  10. 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.CrossRefGoogle Scholar
  11. Deng, S., Huang, L., & Xu, G. (2014). Social network-based service recommendation with trust enhancement. Expert Systems with Applications, 41, 8075–8084.CrossRefGoogle Scholar
  12. 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.CrossRefGoogle Scholar
  13. 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.CrossRefGoogle Scholar
  14. 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.Google Scholar
  15. 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).Google Scholar
  16. 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.CrossRefGoogle Scholar
  17. Gładysz, B. (2017). Fuzzy-probabilistic PERT. Annals of Operations Research, 258(2), 437–452.CrossRefGoogle Scholar
  18. Guldemır, H., & Sengur, A. (2006). Comparison of clustering algorithms for analog modulation classification. Expert Systems with Applications, 30(4), 642–649.CrossRefGoogle Scholar
  19. Hagras, H. A. (2007). Type-2 FLCs: A new generation of fuzzy controllers. IEEE Computational Intelligence Magazine, 2(1), 30–43.CrossRefGoogle Scholar
  20. Halkidi, M., Batistakis, Y., & Vazirgiannis, M. (2001). On clustering validation techniques. Journal of Intelligent Information Systems, 17(2–3), 107–145.CrossRefGoogle Scholar
  21. 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.CrossRefGoogle Scholar
  22. 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.CrossRefGoogle Scholar
  23. 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.CrossRefGoogle Scholar
  24. 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.CrossRefGoogle Scholar
  25. Karnik, N. N., Mandel, J. M., & Liang, Q. (1999). Type-2 fuzzy logic systems. EEE Transactions on Fuzzy Systems, 7(6), 643–658.CrossRefGoogle Scholar
  26. Karnik, N. N., & Mendel, J. M. (2001). Centroid of a type-2 fuzzy set. Information Sciences, 132(1–4), 195–220.CrossRefGoogle Scholar
  27. 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).Google Scholar
  28. 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)Google Scholar
  29. 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.CrossRefGoogle Scholar
  30. 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.CrossRefGoogle Scholar
  31. Mendel, J. M. (2001). Uncertain rule-based fuzzy logic systems: Introduction and new directions. Upper Saddle River: Prentice-Hall.Google Scholar
  32. 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.CrossRefGoogle Scholar
  33. Mobedpour, D., & Ding, C. (2011). User-centered design of a QoS-based web service selection system.1–11. Service Oriented Computing and Applications,. Scholar
  34. Modica, G. D., Tomarchio, O., & Vita, L. (2009). Dynamic SLAs management in service oriented environments. Journal of Systems and Software, 82(5), 759–771. Scholar
  35. 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.CrossRefGoogle Scholar
  36. Oriol, M., Franch, X., & Marco, J. (2015). Monitoring the service-based system lifecycle with SALMon. Expert Systems with Applications, 42(19), 6507–6521.CrossRefGoogle Scholar
  37. 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.CrossRefGoogle Scholar
  38. 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.CrossRefGoogle Scholar
  39. 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.CrossRefGoogle Scholar
  40. 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.Google Scholar
  41. 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.CrossRefGoogle Scholar
  42. 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.CrossRefGoogle Scholar
  43. 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.CrossRefGoogle Scholar
  44. 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. Scholar
  45. 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).Google Scholar
  46. Sharaf, S., & Djemame, K. (2015). Enabling service-level agreement renegotiation through extending WS-Agreement specification. Service Oriented Computing and Applications, 9(2), 177–191.CrossRefGoogle Scholar
  47. 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).Google Scholar
  48. 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).Google Scholar
  49. 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).Google Scholar
  50. 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.CrossRefGoogle Scholar
  51. Sundarraj, R. P. (2002). A model for standardizing human decisions concerning service-contracts management. Annals of Operations Research, 143(1), 171–189.CrossRefGoogle Scholar
  52. Tang, Y., Sun, F., & Sun, Z. (2005). Improved validation index for fuzzy clustering. In American Control Conference (pp. 1120–1125)Google Scholar
  53. 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.CrossRefGoogle Scholar
  54. 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.Google Scholar
  55. 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.CrossRefGoogle Scholar
  56. 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.Google Scholar
  57. 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)Google Scholar
  58. 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.CrossRefGoogle Scholar
  59. 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.CrossRefGoogle Scholar
  60. Wang, W., & Zhang, Y. (2007). On fuzzy cluster validity indices. Fuzzy Sets and Systems, 158(19), 2095–2117.CrossRefGoogle Scholar
  61. 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.CrossRefGoogle Scholar
  62. 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.Google Scholar
  63. Wu, D., & Mendel, J. M. (2007). Uncertainty measures for interval type-2 fuzzy sets. Information Sciences, 177(23), 5378–5393.CrossRefGoogle Scholar
  64. 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.CrossRefGoogle Scholar
  65. Wu, K.-L., & Yang, M.-S. (2005). A cluster validity index for fuzzy clustering. Pattern Recognition Letters, 26(9), 1275–1291.CrossRefGoogle Scholar
  66. Xie, X., & Beni, G. (1991). Validity measure for fuzzy clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 3(8), 841–847.CrossRefGoogle Scholar
  67. Yan, Y., & Chen, M. (2015). Anytime QoS-aware service composition over the GraphPlan. Service Oriented Computing and Applications, 9(1), 1–19.CrossRefGoogle Scholar
  68. Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning-1. Information Sciences, 8(3), 199–249.CrossRefGoogle Scholar
  69. Zadeh, L. A. (2008). Is there a need for fuzzy logic? Information Sciences, 178, 2751–2779.CrossRefGoogle Scholar
  70. 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.CrossRefGoogle Scholar
  71. 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.Google Scholar
  72. Zhang, L., Zou, H., & Yang, F. (2011). A dynamic web service composition algorithm based on TOPSIS. Journal of Networks, 6(9), 1296–1304.Google Scholar
  73. 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.CrossRefGoogle Scholar
  74. 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.CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Centre for Research in Data Science, Computer and Information Sciences DepartmentUniversiti Teknologi PETRONASPerakMalaysia

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