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

Author

Listed:
  • Mohd Hilmi Hasan

    (Universiti Teknologi PETRONAS)

  • Jafreezal Jaafar

    (Universiti Teknologi PETRONAS)

  • Junzo Watada

    (Universiti Teknologi PETRONAS)

  • Mohd Fadzil Hassan

    (Universiti Teknologi PETRONAS)

  • Izzatdin Abdul Aziz

    (Universiti Teknologi PETRONAS)

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.

Suggested Citation

  • Mohd Hilmi Hasan & Jafreezal Jaafar & Junzo Watada & Mohd Fadzil Hassan & Izzatdin Abdul Aziz, 2021. "An interval type-2 fuzzy model of compliance monitoring for quality of web service," Annals of Operations Research, Springer, vol. 300(2), pages 415-441, May.
    • Handle: RePEc:spr:annopr:v:300:y:2021:i:2:d:10.1007_s10479-019-03328-6
    DOI: 10.1007/s10479-019-03328-6
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    References listed on IDEAS

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    1. Michał Jakubczyk & Bogumił Kamiński, 2017. "Fuzzy approach to decision analysis with multiple criteria and uncertainty in health technology assessment," Annals of Operations Research, Springer, vol. 251(1), pages 301-324, April.
    2. Anil Jindal & Kuldip Singh Sangwan, 2017. "Multi-objective fuzzy mathematical modelling of closed-loop supply chain considering economical and environmental factors," Annals of Operations Research, Springer, vol. 257(1), pages 95-120, October.
    3. Shilian Han & Jerry Mendel, 2012. "A new method for managing the uncertainties in evaluating multi-person multi-criteria location choices, using a perceptual computer," Annals of Operations Research, Springer, vol. 195(1), pages 277-309, May.
    4. Youngseok Choi & Habin Lee & Zahir Irani, 2018. "Big data-driven fuzzy cognitive map for prioritising IT service procurement in the public sector," Annals of Operations Research, Springer, vol. 270(1), pages 75-104, November.
    5. Adil Baykasoğlu & İlker Gölcük & Derya Eren Akyol, 2017. "A fuzzy multiple-attribute decision making model to evaluate new product pricing strategies," Annals of Operations Research, Springer, vol. 251(1), pages 205-242, April.
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