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A Novel Outlier-Tolerable and Predictive Approach to Web Service Composition

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Web Services – ICWS 2022 (ICWS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13736))

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Abstract

The Quality-of-Service (QoS) aspects of Web service has gained popularity in the field of service computing. QoS-oriented Web service composition is a distributed model to construct new web service on top of existing primitive or other composite web services with QoS guarantees. A major challenge in this field is that the QoS data of candidate services are with run-time fluctuations and thus difficult to predict. Traditional approaches in this direction tended to address the challenge by statistics, prediction and neural network-based models. A major limitation of these methods lies in that they ignore outliers data in the historical QoS data, in terms of inconsistencies, errors, shifts, corruptions, etc. In this work, instead, we consider outliers in QoS series to be non-neglectable, and propose an outlier-tolerable and predictive approach to service composition through leveraging a joint estimation-based outlier detection method and a niched genetic algorithm. To validate the effectiveness of our proposed method, we conduct extensive case studies based on different outlier conditions, and the experimental results show that our method is superior to existing ones.

This work is supported by National Science Foundations with No. 62172062 and No. 62162036, and Chongqing Normal University Foundation with No. 22XLB016. Yunni Xia is the first corresponding author (email: xiayunni@hotmail.com). Peng Chen is the second corresponding author (email: chenpeng@mail.xhu.edu.cn).

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Authors and Affiliations

  1. School of Computer and Information Science, Chongqing Normal University, Chongqing, 401331, China

    Xiaoning Sun

  2. School of Computer and Software Engineering, Xihua University, Chengdu, 610039, China

    Peng Chen, Xianhua Niu & Juan Chen

  3. School of Computer and Information Engineering, Jiangxi Normal University, Nanchang, 330022, China

    Mengxuan Dai

  4. College of Computer Science, Chongqing University, Chongqing, 400030, China

    Yunni Xia & Hong Xie

  5. Data Science Research Center, Faculty of Science, Kunming University of Science and Technology, Kunming, 650031, China

    Wanbo Zheng & Xiaodong Fu

  6. Global Energy Interconnection Research Institute Co. Ltd., Beijing, 102209, China

    Jianqi Li

  7. Engineering college, Huaqiao University, Quanzhou, 362021, China

    Kai Peng

Authors
  1. Xiaoning Sun
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Correspondence to Peng Chen or Yunni Xia .

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Editors and Affiliations

  1. Beijing University of Posts and Telecommunications, Beijing, China

    Yuchao Zhang

  2. Kingdee International Software Group Co., Ltd., Shenzhen, China

    Liang-Jie Zhang

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Sun, X. et al. (2022). A Novel Outlier-Tolerable and Predictive Approach to Web Service Composition. In: Zhang, Y., Zhang, LJ. (eds) Web Services – ICWS 2022. ICWS 2022. Lecture Notes in Computer Science, vol 13736. Springer, Cham. https://doi.org/10.1007/978-3-031-23579-5_2

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  • DOI: https://doi.org/10.1007/978-3-031-23579-5_2

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