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The performance of Internet services—be it file download completion times, video quality, or lag-free video conferencing—is heavily influenced by network parameters. These include the bottleneck bandwidth, network delays, and how fairly the bottleneck link is shared with other services. However, current techniques to evaluate service performance in emulated and simulated networks suffer from three major issues: (a) testing predominantly in settings representing the "edge" of the Internet, and not the core; (b) focus on evaluating Congestion Control Algorithms (CCAs), neglecting the impact of application-level controls like Adaptive-Bitrate (ABR) algorithms on network performance; (c) testing in settings that do not necessarily reflect the network conditions experienced by services with expansive CDNs. The goal of this thesis is to improve the state of the art in emulated testing for a more up-to-date evaluation of Internet service performance. To highlight the need to perform Internet evaluations in settings representing congestion at the core of the Internet, we test CCAs with core Internet speeds and flow counts. We find that this dramatically alters fairness outcomes, and challenges long-standing assumptions about CCA behavior that were built on measurements performed at in settings representing the edge of the Internet, emphasizing the need to run Internet evaluations in more diverse settings. We then challenge the implicit assumption that CCA evaluations alone are suf- ficient to predict the network behavior of services that use them. We perform this analysis through the lens of fairness, and build Prudentia, an Internet fairness watch- dog, that measures how fairly two Internet services can share a bottleneck link. In addition to discovering extreme unfairness on the Internet today, we gain key insights into improving current testing methodology – (a) The most and least fair services both use variants of the same CCA, highlighting the need to test services in addition to CCAs; (b) network settings can drastically affect even service-level fairness outcomes, necessitating their careful selection. Lastly, we infer the network conditions experienced by users of Netflix, a global video streaming provider, and contrast them with those used in typical Internet evaluations. We find that Netflix users experience shorter RTTs, greater maximum observed queuing delay, and greater ACK aggregation, all parameters that play an important role in determining CCA behavior. This highlights the need for more service operators to run similar analyses and share their respective perspectives of prevalent network conditions, so that the networking community can include these settings in the design and evaluation of Internet services. 

Much of our understanding of congestion control algorithm (CCA) throughput and fairness is derived from models and measurements that (implicitly) assume congestion occurs in the last mile. That is, these studies evaluated CCAs in “small scale” edge settings at the scale of tens of flows and up to a few hundred Mbps bandwidths. However, recent measurements show that congestion can also occur at the core of the Internet on inter-provider links, where thousands of flows share high bandwidth links. Hence, a natural question is: Does our understanding of CCA throughput and fairness continue to hold at the scale found in the core of the Internet, with 1000s of flows and Gbps bandwidths? Our preliminary experimental study finds that some expectations derived in the edge setting do not hold at scale. For example, using loss rate as a parameter to the Mathis model to estimate TCP NewReno throughput works well in edge settings, but does not provide accurate throughput estimates when thousands of flows compete at high bandwidths. In addition, BBR – which achieves good fairness at the edge when competing solely with other BBR flows – can become very unfair to other BBR flows at the scale of the core of the Internet. In this paper, we discuss these results and others, as well as key implications for future CCA analysis and evaluation.