Network Tomography

Identifiability, Measurement Design, and Network State Inference

Inbunden, Engelska, 2021

Av Ting He, Liang Ma, Ananthram Swami, Don Towsley, Ting (Pennsylvania State University) He, Amherst) Towsley, Don (University of Massachusetts

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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.

Produktinformation

Utgivningsdatum2021-05-27
Mått250 x 175 x 20 mm
Vikt590 g
FormatInbunden
SpråkEngelska
Antal sidor330
FörlagCambridge University Press
ISBN9781108421485

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Ting He is an Associate Professor in the School of Electrical Engineering and Computer Science at The Pennsylvania State University. She is a Senior Member of the IEEE. Liang Ma is a Research Scientist in the AI Group at Dataminr Inc. Ananthram Swami is the Senior Research Scientist for Network Science at the US Army's CCDC Army Research Laboratory. He is an ARL Fellow and a Fellow of the IEEE. Don Towsley is a Distinguished Professor of Computer Science at the University of Massachusetts, Amherst. He is a Fellow of the IEEE and ACM.

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.