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Measuring Connectivity in the Primary Visual Pathway in Human Albinism Using Diffusion Tensor Imaging and Tractography
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Adaptive Path Selection for Link Loss Inference in Network Tomography Applications.

Yan Qiao1,2, Jun Jiao1, Yuan Rao1

  • 1School of Information and Computer Science, Anhui Agricultural University, Hefei, Anhui, China.

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This study introduces an adaptive path selection method for network tomography to reduce traffic overhead. The new approach efficiently identifies optimal paths, improving performance and accuracy in link loss inference.

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Area of Science:

  • Computer Science
  • Network Engineering

Background:

  • Network tomography infers link loss rates from path loss rates.
  • End-to-end path measurements can cause significant network traffic overhead.
  • Existing methods may be inefficient by not utilizing paths with zero loss.

Purpose of the Study:

  • To develop an optimal path selection strategy for network tomography.
  • To minimize traffic overhead while maximizing the accuracy of link loss inference.
  • To improve upon classical simultaneous path selection methods.

Main Methods:

  • Proposed an adaptive, sequential path selection algorithm.
  • Developed a theorem for efficient path selection.
  • Utilized graph construction and decomposition techniques.
  • Compared performance against classical methods via simulations.

Main Results:

  • The adaptive method significantly outperforms the classical approach.
  • Demonstrated improvements in probing cost and accuracy of determined links.
  • Showcased enhanced running speed compared to traditional methods.

Conclusions:

  • The proposed adaptive path selection method is more efficient and accurate.
  • This approach offers a cost-effective solution for network tomography.
  • The findings suggest a new standard for path selection in network monitoring.