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
8 - Stochastic Network Tomography Using Multicast Measurements
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
In contrast to unicast measurements considered in Chapter 7, this chapter focuses on stochastic network tomography based on multicast measurements, where each probe is sent along a multicast tree from one source to multiple destinations, duplicated at each intermediate node with at least two outgoing links. Using loss tomography as an example, the chapter details how the correlations between the measurements at different destinations sharing links in the multicast tree can be utilized to infer link loss rates, while briefly discussing how this approach applies to other performance metrics. Moreover, this chapter further illustrates how correlated loss observations obtained from multicast probes can be used to reliably infer the topology of the multicast tree, which belongs to another branch of network tomography (network topology tomography) that will be formally introduced in Chapter 9, and complements the high-level discussions there.
- Type
- Chapter
- Information
- Network TomographyIdentifiability, Measurement Design, and Network State Inference, pp. 201 - 217Publisher: Cambridge University PressPrint publication year: 2021