IDEAS home Printed from https://ideas.repec.org/a/spr/infosf/v19y2017i6d10.1007_s10796-017-9767-x.html
   My bibliography  Save this article

Reliable and efficient big service selection

Author

Listed:
  • Ling Huang

    (Beijing University of Posts and Telecommunications)

  • Qinglin Zhao

    (Macau University of Science and Technology)

  • Yan Li

    (Shanghai International Studies University)

  • Shangguang Wang

    (Beijing University of Posts and Telecommunications)

  • Lei Sun

    (Beijing University of Posts and Telecommunications)

  • Wu Chou

    (Huawei Technologies Co., Ltd.)

Abstract

Big services, both virtual (e.g., cloud services) and physical (e.g., public transportation), are evolving rapidly to handle and deal with big data. By aggregating services from various domains, big services adopt selection schemes to produce composite service solutions that meet customer requirements. However, unlike traditional service selection, a huge number of big services require some lengthy selection processes to improve the service reliability. In this paper, we propose an efficient big service selection approach based on the coefficient of variation and mixed integer programming that improves the solution in two senses: 1) minimizing the time cost and 2) maximizing the reliability. We tested our approach on real-world datasets, and the experimental results indicated that our approach is superior to others.

Suggested Citation

  • Ling Huang & Qinglin Zhao & Yan Li & Shangguang Wang & Lei Sun & Wu Chou, 2017. "Reliable and efficient big service selection," Information Systems Frontiers, Springer, vol. 19(6), pages 1273-1282, December.
    • Handle: RePEc:spr:infosf:v:19:y:2017:i:6:d:10.1007_s10796-017-9767-x
    DOI: 10.1007/s10796-017-9767-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10796-017-9767-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10796-017-9767-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shih-Chia Huang & Suzanne McIntosh & Stanislav Sobolevsky & Patrick C. K. Hung, 2017. "Big Data Analytics and Business Intelligence in Industry," Information Systems Frontiers, Springer, vol. 19(6), pages 1229-1232, December.
    2. Firth, Anton & Zhang, Bo & Yang, Aidong, 2019. "Quantification of global waste heat and its environmental effects," Applied Energy, Elsevier, vol. 235(C), pages 1314-1334.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:infosf:v:19:y:2017:i:6:d:10.1007_s10796-017-9767-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.