Advertisement

Service Evaluation-Based Resource Selection in Cloud Manufacturing

  • Yan-Wei Zhao
  • Li-Nan Zhu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8683)

Abstract

With the development of cloud computing, cloud manufacturing has been gained more and more attention. According to the peculiarity of cloud manufacturing, the resource designated as manufacturing service is always massive, complex and heterogeneous, and the high degree of user participation, and user diversity are also the main features. This paper presents a method of resource selection based on service evaluation, which consists of predictive evaluation and recommended evaluation. In detail, predictive evaluation is based on user’s historical service evaluations which may have different influence according to the experience in different time. Recommended evaluation is given by the recommenders who are generated by 2-step selection and have different recommended weight according to their similarities and objectivities. Finally, experiment results show that the proposed algorithm has better performance.

Keywords

cloud manufacturing resource selection service evaluation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Zhu, L.N., Zhao, Y.W., Wang, W.L.: A bilayer resource model of cloud man-ufacturing services. Math. Probl. Eng., Article ID: 607582, 10 pages (2013), doi: 10.1155/2013/607582Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Yan-Wei Zhao
    • 1
  • Li-Nan Zhu
    • 1
    • 2
    • 3
  1. 1.Key Laboratory of Special Purpose Equipment and Advanced Processing Technology, Ministry of EducationZhejiang University of TechnologyHangzhouP.R.China
  2. 2.College of Computer Science and TechnologyZhejiang University of TechnologyHangzhouP.R.China
  3. 3.College of Educational Science and TechnologyZhejiang University of TechnologyHangzhouP.R.China

Personalised recommendations