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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.
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Honey & Spice
Bolu Babalola
Paperback
(1)
R469
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