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Taming Both Predictable and Unpredictable Link Failures for Network Tomography

  • Published:2018-06 Issue:3 Volume:26 Page:1460-1473
  • ISSN:1063-6692
  • Container-title:IEEE/ACM Transactions on Networking
  • language:
  • Short-container-title:IEEE/ACM Trans. Networking

Author:

Li HuikangORCID,Gao YiORCID,Dong WeiORCID,Chen Chun

Funder

National Science Foundation of China

Zhejiang Provincial Natural Science Foundation of China

Alibaba-Zhejiang University Joint Institute of Frontier Technologies

Zhejiang Provincial Key Research and Development Program

Fundamental Research Funds for the Central Universities

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Computer Science Applications,Software

Cited by 13 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. DRL-Tomo: a deep reinforcement learning-based approach to augmented data generation for network tomography;The Computer Journal;2024-07-21

2. A Bayesian Approach to Network Monitoring for Progressive Failure Localization;IEEE/ACM Transactions on Networking;2023-04

3. Bound-Based Network Tomography for Inferring Interesting Path Metrics;IEEE/ACM Transactions on Networking;2023-02

4. Locating High-loss Links for OpenFlow Networks by Multiple Hosts to Probe Packets;2022 24th International Conference on Advanced Communication Technology (ICACT);2022-02-13

5. A Boolean Network Tomography based Method for Deterministic Multi-point Fault Detection;2021 IEEE Global Communications Conference (GLOBECOM);2021-12
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