This chapter introduces the definition of network tomography and the three branches of network tomography and provides an overview of the main issues addressed in the subsequent chapters.

Boolean network tomography is another well-studied branch of network tomography, which addresses the inference of binary performance indicators (e.g., normal vs. failed, or uncongested vs. congested) of internal network elements from the corresponding binary performance indicators on measurement paths. Boolean network tomography fundamentally differs from additive network tomography in that it is a Boolean linear system inversion problem in which each measurement path only provides one bit of information and hence deserves a separate discussion. This chapter introduces a series of identifiability measures (e.g., k-identifiability, maximum identifiability index) to quantify the capability of Boolean network tomography in uniquely detecting and localizing failed/congested network elements. As the definitions of these identifiability measures are combinatorial in nature and hard to verify for large networks, the discussion focuses on polynomial-time verifiable conditions and computable bounds, as well as the associated algorithms.

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.

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.

This chapter covers preliminary materials required to understand the presentation in the following chapters, including selected definitions from graph theory, linear algebra, and parameter estimation. We also introduce a classification of routing mechanisms based on the controllability of the routing of probes by monitors generating the probes, which will facilitate the discussion in the following chapters.

Based on the identifiability measures for Boolean network tomography presented in Chapter 5, this chapter addresses the follow-up question of how to design the measurement system to optimize the identifiability measure of interest, with a focus on the placement of monitoring nodes.Depending on the mechanism to collect measurements, the problem is divided into (1) monitor placement, (2) beacon placement, and (3) monitoring-aware service placement, where the first approach requires monitoring nodes at both endpoints of each measurement path, the second approach requires a monitoring node only at one of the endpoints of each measurement path, and the third approach requires each measurement path to be the default routing path between a client and a server. As many of such problems are NP-hard, the focus is put on establishing the hardness of the optimal solution and developing polynomial-time suboptimal algorithms with performance guarantees. The chapter also covers a suite of path construction problems addressing how to construct or select measurement paths to optimize the tradeoff between identifiability and probing cost.

This section lists all major events that are in some way related to Automotive Ethernet. The idea is to give the readers a perspective on the development predecessing and in parallel to the introduction of Automotive Ethernet.

This chapter gives a personal outlook on how the authors see the changes, chances, and challenges in the automotive industry in relation to the introduction of Automotive Ethernet.

This chapter enlightens the framework that allowed Ethernet to be introduced as a new in-vehicle networking technology. It explains the early use cases as well as the infrastructure that was set up in order to support the proliferation of Automotive Ethernet in the automotive industry.

Before adopting Automotive Ethernet, the automotive industry had developed and used a number of in-vehicle networking technologies. This chapter explains why and how in-vehicle networking was done before the advent of Automotive Ethernet. Furthermore, it explains the eco-system the automotive industry is used to working in when adopting new technologies.