Network Tomography
Identifiability, Measurement Design, and Network State Inference

A rigorous yet accessible treatment of the fundamental theory and algorithms of network tomography.

Ting He (Author), Liang Ma (Author), Ananthram Swami (Author), Don Towsley (Author)

9781108421485, Cambridge University Press

Hardback, published 27 May 2021

330 pages
17.5 x 25 x 2 cm, 0.59 kg

Providing the first truly comprehensive overview of Network Tomography - a novel network monitoring approach that makes use of inference techniques to reconstruct the internal network state from external vantage points - this rigorous yet accessible treatment of the fundamental theory and algorithms of network tomography covers the most prominent results demonstrated on real-world data, including identifiability conditions, measurement design algorithms, and network state inference algorithms, alongside practical tools for applying these techniques to real-world network management. It describes the main types of mathematical problems, along with their solutions and properties, and emphasizes the actions that can be taken to improve the accuracy of network tomography. With proofs and derivations introduced in an accessible language for easy understanding, this is an essential resource for professional engineers, academic researchers, and graduate students in network management and network science.

Introduction
1. Preliminaries
2. Fundamental conditions for additive network tomography
3. Monitor placement for additive network tomography
4. Measurement path construction for additive network tomography
5. Fundamental conditions for Boolean network tomography
6. Measurement design for Boolean network tomography
7. Stochastic network tomography using unicast measurements
8. Stochastic network tomography using multicast measurements
9. Other applications and miscellaneous techniques
Appendix datasets for evaluations
Index.

Subject Areas: Computer networking & communications [UT], Electronics & communications engineering [TJ]