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Multi-user web service selection based on multi-QoS prediction

Author

Listed:
  • Shangguang Wang

    (Beijing University of Posts and Telecommunications)

  • Ching-Hsien Hsu

    (Chung Hua University)

  • Zhongjun Liang

    (Beijing University of Posts and Telecommunications)

  • Qibo Sun

    (Beijing University of Posts and Telecommunications)

  • Fangchun Yang

    (Beijing University of Posts and Telecommunications)

Abstract

In order to find best services to meet multi-user’s QoS requirements, some multi-user Web service selection schemes were proposed. However, the unavoidable challenges in these schemes are the efficiency and effect. Most existing schemes are proposed for the single request condition without considering the overload of Web services, which cannot be directly used in this problem. Furthermore, existing methods assumed the QoS information for users are all known and accurate, and in real case, there are always many missing QoS values in history records, which increase the difficulty of the selection. In this paper, we propose a new framework for multi-user Web service selection problem. This framework first predicts the missing multi-QoS values according to the historical QoS experience from users, and then selects the global optimal solution for multi-user by our fast match approach. Comprehensive empirical studies demonstrate the utility of the proposed method.

Suggested Citation

  • Shangguang Wang & Ching-Hsien Hsu & Zhongjun Liang & Qibo Sun & Fangchun Yang, 2014. "Multi-user web service selection based on multi-QoS prediction," Information Systems Frontiers, Springer, vol. 16(1), pages 143-152, March.
    • Handle: RePEc:spr:infosf:v:16:y:2014:i:1:d:10.1007_s10796-013-9455-4
    DOI: 10.1007/s10796-013-9455-4
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    References listed on IDEAS

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    1. Daniel D. Lee & H. Sebastian Seung, 1999. "Learning the parts of objects by non-negative matrix factorization," Nature, Nature, vol. 401(6755), pages 788-791, October.
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    Cited by:

    1. Michael Bortlik & Bernd Heinrich & Michael Mayer, 2018. "Multi User Context-Aware Service Selection for Mobile Environments," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 60(5), pages 415-430, October.
    2. Junwen Lu & Guanfeng Liu & Keshou Wu & Wenjiang Qin, 2019. "Location-Aware Web Service Composition Based on the Mixture Rank of Web Services and Web Service Requests," Complexity, Hindawi, vol. 2019, pages 1-16, April.
    3. Ching-Hsien Hsu & Jianhua Ma & Mohammad S. Obaidat, 2014. "Dynamic intelligence towards merging cloud and communication services," Information Systems Frontiers, Springer, vol. 16(1), pages 1-5, March.

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