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
9 - Other Applications and Miscellaneous Techniques
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
This chapter covers other canonical applications of network tomography that have been studied in the literature but fallen out of the scope of the previous chapters. This includes the inference of network routing topology (network topology tomography) and the inference of traffic demands (traffic matrix or origin-destination tomography). It also covers miscellaneous techniques used in network tomography that are not covered in the previous chapters (e.g., network coding). The chapter then concludes the book with discussions on practical issues in the deployment of tomography-based monitoring systems and future directions in addressing these issues.
Keywords
- Type
- Chapter
- Information
- Network TomographyIdentifiability, Measurement Design, and Network State Inference, pp. 218 - 225Publisher: Cambridge University PressPrint publication year: 2021