Network Tomography is a novel network monitoring approach that reconstructs the internal network state from external vantage points. This rigorous yet accessible treatment covers the main types of mathematical problems, solutions, and properties, and emphasizes the actions to improve accuracy. It is an essential resource for professional engineers, academic researchers, and graduate students in network management and network science.

Format: Hardback
Length: 330 pages
Publication date: 27 May 2021
Publisher: Cambridge University Press

Network Tomography is a groundbreaking network monitoring approach that reconstructs the internal network state from external vantage points using inference techniques. This comprehensive yet accessible treatment delves into the fundamental theory and algorithms of network tomography, covering prominent results demonstrated on real-world data. It discusses identifiability conditions, measurement design algorithms, and network state inference algorithms, alongside practical tools for applying these techniques to real-world network management. The book also outlines the main types of mathematical problems, their solutions, and properties, emphasizing actions to improve the accuracy of network tomography. With proofs and derivations presented in an accessible language, this essential resource is tailored for professional engineers, academic researchers, and graduate students in network management and network science.

Network Tomography: Reconstructing Network States from External Observations

Network Tomography is a novel network monitoring approach that utilizes inference techniques to reconstruct the internal network state from external vantage points. It provides a comprehensive overview of the fundamental theory and algorithms involved in this field, covering topics such as identifiability conditions, measurement design algorithms, and network state inference algorithms. The book also presents practical tools for applying these techniques to real-world network management, including examples and case studies.

One of the key strengths of Network Tomography is its ability to provide a comprehensive understanding of network behavior. By leveraging external observations, such as traffic flow or packet captures, network tomography can reconstruct the internal network state, including node locations, link capacities, and traffic patterns. This information can be invaluable for network administrators, engineers, and researchers in understanding the performance, reliability, and security of networks.

The book begins by introducing the basic concepts of network tomography and its applications. It then delves into the mathematical foundations of the field, including probability theory, linear algebra, and optimization. The authors provide proofs and derivations to explain the theoretical concepts in an accessible language, making the book suitable for professional engineers, academic researchers, and graduate students in network management and network science.

In the subsequent chapters, the book discusses the main types of mathematical problems encountered in network tomography. These problems include identifiability conditions, which determine whether a network can be reconstructed from external observations, and measurement design algorithms, which determine the set of observations required to reconstruct the network state. The authors also explore network state inference algorithms, which use the reconstructed network state to estimate unknown parameters, such as node locations or link capacities.

Throughout the book, the authors emphasize the importance of practical tools for applying network tomography in real-world scenarios. They discuss measurement systems, data collection, and data analysis techniques, as well as software tools and frameworks that can be used to implement network tomography algorithms. The book also includes examples and case studies that demonstrate the practical applications of network tomography in various network environments, such as wireless networks, internet networks, and enterprise networks.

One of the key advantages of Network Tomography is its ability to provide insights into network behavior that are not possible with traditional network monitoring approaches. For example, it can help identify bottlenecks, congestion points, and other areas of poor performance in networks. It can also be used to optimize network configurations, such as routing tables or network bandwidth, to improve network performance.

In conclusion, Network Tomography is a groundbreaking approach to network monitoring that reconstructs the internal network state from external vantage points using inference techniques. This comprehensive yet accessible treatment of the fundamental theory and algorithms of network tomography covers the most prominent results demonstrated on real-world data, alongside practical tools for applying these techniques to real-world network management. 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.

Weight: 594g
Dimension: 177 x 252 x 22 (mm)
ISBN-13: 9781108421485