Book contents
- Frontmatter
- Contents
- Notation
- 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
3 - Monitor Placement for Additive Network Tomography
Published online by Cambridge University Press: 25 May 2021
- Frontmatter
- Contents
- Notation
- 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
Summary
Based on the conditions for identifying additive link metrics presented in Chapter 2, this chapter addresses two network design questions: (1) Given an unbounded number of monitors, where should they be placed in the network to identify the metrics of all the links using a minimum number of monitors? (2) Given a bounded number of monitors, where should they be placed in the network to identify the metrics of the largest subset of links? The focus here is on the design of intelligent algorithms that can efficiently compute the optimal monitor locations without enumerating all possible monitor placements, achieved through strategic decomposition of the network topology based on the required identifiability conditions. Variations of these algorithms are also given to address cases with predictable or unpredictable topology changes and limited links of interest. In addition to theoretical analysis, empirical results are given to demonstrate the capability of selected algorithms for which such results are available.
Keywords
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- Chapter
- Information
- Network TomographyIdentifiability, Measurement Design, and Network State Inference, pp. 43 - 77Publisher: Cambridge University PressPrint publication year: 2021