As the Internet has grown to represent arguably the largest “engineered” system on earth, network researchers have shown increasing interest in measuring this large-scale networked system. In the process, structures such as the physical Internet or the many different (logical) overlay networks that this physical infrastructure enables have been the focus of numerous studies. Many of these studies have been fueled by the ease of access to “big data”. Moreover, they benefited from advances in the study of complex networks. However, an important missing aspect in typical applications of complex network theory to the study of real-world distributed systems has been a general lack of attention to domain knowledge. On the one hand, missing or superficial domain knowledge can negatively affect the studies “input”; that is, limitations or idiosyncrasies of the measurement methods can render the resulting graphs difficult to interpret if not meaningless. On the other hand, lacking or insufficient domain knowledge can result in specious “output”; that is, popular graph abstractions of real-world systems are incapable of accounting for “details” that are important from an engineering perspective. In this thesis, we take a closer look at measurement-based characterization of a few real-world large-scale networked systems and focus on the role that domain knowledge plays in gaining a thorough understanding of these systems key properties and behavior. More specifically, we use domain knowledge to (i) design context-aware measurement strategies that capture the relevant information about the system of interest, (ii) analyze the captured view of the networked system baring in mind the abstraction imposed by the chosen graph representation, and (iii) scrutinize the results derived from the analysis of the graph-based representations by investigating the root causes underlying these findings. The main technical contribution of our work is twofolds. First, we establish concrete connections between the amount and level of domain knowledge needed and the quality of the measurements collected from networked systems. Second, we also provide concrete evidence for the role that domain knowledge plays in the analysis of views inferred from measurements collected from large-scale networked systems

Over the last few decades, the Internet ecosystem has been continuously evolving to meet the demands of its ever-increasing user base. Drastic changes in the Internet infrastructure have improved its capacity and throughput performance, enabling a wealth of new services. For Internet Service Providers (ISPs), anticipating and accommodating the rapidly shifting traffic demands has been a technological, economical, and political challenge. Thus far, this challenge has been met in an "organic" fashion, for the most part, based on unilateral actions of many different players such as ISPs, content providers, public policy makers, international organizations, and large enterprises. This symbiotic relationship among many and often competing change factors has led to a system of enormous complexity that was not a product of well-founded engineering principles. Despite the continuous efforts of the scientific and enterprise communities to discover and to model the Internet, understanding its structure remains a hard challenge. In this thesis, we provide a new perspective on the Internet's evolutionary pat- terns at the Autonomous System (AS) level. While many studies have focused on the mathematical models that express the growth of the AS graph topology as a whole, little research has been performed to correlate this growth with geographic, economic, and political data, as well as related business interests. We divide the Internet to five distinct regions using the well-established Internet registry classification and show that the structural properties and evolutionary patterns differ from region to region. We further analyze the business relationships that dominate each region, as well relationships between regions. Conclusions from our analysis is used to explain global as well as local Internet structure phenomena.

The Internet consists of thousands of autonomous networks connected together to provide end-to-end reachability. Networks of different sizes, and with different functions and business objectives, interact and co-exist in the evolving "Internet Ecosystem". The Internet ecosystem is highly dynamic, experiencing growth (birth of new networks), rewiring (changes in the connectivity of existing networks), as well as deaths (of existing networks). The dynamics of the Internet ecosystem are determined both by external "environmental" factors (such as the state of the global economy or the popularity of new Internet applications) and the complex incentives and objectives of each network. These dynamics have major implications on how the future Internet will look like. How does the Internet evolve? What is the Internet heading towards, in terms of topological, performance, and economic organization? How do given optimization strategies affect the profitability of different networks? How do these strategies affect the Internet in terms of topology, economics, and performance? In this thesis, we take some steps towards answering the above questions using a combination of measurement and modeling approaches. We first study the evolution of the Autonomous System (AS) topology over the last decade. In particular, we classify ASes and inter-AS links according to their business function, and study separately their evolution over the last 10 years. Next, we focus on enterprise customers and content providers at the edge of the Internet, and propose algorithms for a stub network to choose its upstream providers to maximize its utility (either monetary cost, reliability or performance). Third, we develop a model for interdomain network formation, incorporating the effects of economics, geography, and the provider/peer selections strategies of different types of networks. We use this model to examine the "outcome" of these strategies, in terms of the topology, economics and performance of the resulting internetwork. We also investigate the effect of external factors, such as the nature of the interdomain traffic matrix, customer preferences in provider selection, and pricing/cost structures. Finally, we focus on a recent trend due to the increasing amount of traffic flowing from content providers (who generate content), to access providers (who serve end users). This has led to a tussle between content providers and access providers, who have threatened to prioritize certain types of traffic, or charge content providers directly -- strategies that are viewed as violations of "network neutrality". In our work, we evaluate various pricing and connection strategies that access providers can use to remain profitable without violating network neutrality.

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