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Research on evaluation index method of cloud-network convergence capability.

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This study introduces a new index system for cloud-network convergence, enhancing evaluation stability and providing crucial reference weights for future development. The improved methods ensure reliable cloud-network convergence assessments.

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Area of Science:

  • Computer Science
  • Network Engineering
  • Systems Engineering

Background:

  • Current cloud-network convergence lacks a robust index system for guidance and development.
  • Selecting and evaluating cloud-network convergence indexes is a complex challenge requiring specialized schemes.

Purpose of the Study:

  • To propose an improved index selection and evaluation scheme for cloud-network convergence.
  • To enhance the stability and reliability of index evaluation, particularly in the presence of abnormal data.

Main Methods:

  • Developed an improved analytic hierarchy process (i-AHP) for index selection.
  • Utilized expert experience, geometric mean, and least squares for scheme development.
  • Introduced an improved weighted least square method (WLSM) for index evaluation stability.

Main Results:

  • The WLSM demonstrated superior stability for index evaluation, even with abnormal data.
  • The proposed i-AHP and WLSM schemes effectively avoid weight deviation caused by data fluctuations.
  • Index weights derived from the WLSM offer a valuable reference for cloud-network convergence development.

Conclusions:

  • The developed i-AHP and WLSM provide a more stable and reliable method for evaluating cloud-network convergence.
  • The proposed schemes address the need for a measurable and evaluable index system in this domain.
  • This research offers practical guidance and reference weights for advancing cloud-network convergence infrastructure.