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1. Battle for Bandwidth: On The Deployability of New Congestion Control Algorithms

   Ware, Ranysha J (August 2025, Carnegie Mellon University)

   The Internet has become the central source of information and communication in modern society. Congestion control algorithms (CCAs) are critical for the stability of the Internet: ensuring that users can fairly and efficiently share the network. Over the past 30 years, researchers and Internet content providers have proposed and deployed dozens of new CCAs designed to keep up with the growing demands of faster networks, diverse applications, and mobile users. Without tools to understand this growing heterogeneity in CCAs deployed on the Internet, the fairness of the Internet is at stake. Towards understanding this growing heterogeneity, we develop CCAnalyzer, a tool to determine what CCA a particular web service deploys, outperforming previous classifiers in accuracy and efficiency. With CCAnalyzer, we show that new CCAs, both known and unknown, have widespread deployment on the Internet today, including a recently proposed CCA by Google: BBRv1. Next, we develop the first model of BBRv1, and prove BBRv1 can be very unfair to legacy loss-based CCAs, an alarming finding given the prolific deployment of BBRv1. Consequently, we argue the need for a better methodology for determining if a new CCA is safe to deploy on the Internet today. We describe how the typical methodology testing for equal-rate fairness (every user gets the same bandwidth) is both an unachievable goal and ultimately, not the right threshold for determining if a new CCA is safe to deploy alongside others. Instead of equal-rate fairness, we propose a new metric we call, harm, and argue for a harm-based threshold. Lastly, we present RayGen, a novel framework for evaluating interactions between heterogeneous CCAs. RayGen uses a genetic algorithm to efficiently explore the large state space of possible workloads and network settings when two CCAs compete. With a small budget of experiments, RayGen finds more harmful scenarios than a parameter sweep and random search. 

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2. A Pairwise Fair and Community-preserving Approach to k-Center Clustering

   Brubach, B; Chakrabarti, D; Dickerson, J; Khuller, S; Srinivasan, A; Tsepenekas, L (July 2020, International Conference on Machine Learning (ICML))

   null (Ed.)

   Clustering is a foundational problem in machine learning with numerous applications. As machine learning increases in ubiquity as a back-end for automated systems, concerns about fairness arise. Much of the current literature on fairness deals with discrimination against protected classes in supervised learning (group fairness). We define a different notion of fair clustering wherein the probability that two points (or a community of points) become separated is bounded by an increasing function of their pairwise distance (or community diameter). We capture the situation where data points represent people who gain some benefit from being clustered together. Unfairness arises when certain points are deterministically separated, either arbitrarily or by someone who intends to harm them as in the case of gerrymandering election districts. In response, we formally define two new types of fairness in the clustering setting, pairwise fairness and community preservation. To explore the practicality of our fairness goals, we devise an approach for extending existing k-center algorithms to satisfy these fairness constraints. Analysis of this approach proves that reasonable approximations can be achieved while maintaining fairness. In experiments, we compare the effectiveness of our approach to classical k-center algorithms/heuristics and explore the tradeoff between optimal clustering and fairness. 

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3. Modeling BBR’s Interactions with Loss-Based Congestion Control

   https://doi.org/10.1145/3355369.3355604  

   Ware, Ranysha; Mukerjee, Matthew K; Seshan, Srinivasan; Sherry, Justine (January 2019, Proceedings of the ACM SIGCOMM Internet Measurement Conference)

   BBR is a new congestion control algorithm (CCA) deployed for Chromium QUIC and the Linux kernel. As the default CCA for YouTube (which commands 11+% of Internet traffic), BBR has rapidly become a major player in Internet congestion control. BBR’s fairness or friendliness to other connections has recently come under scrutiny as measurements from multiple research groups have shown undesirable outcomes when BBR competes with traditional CCAs. One such outcome is a fixed, 40% proportion of link capacity consumed by a single BBR flow when competing with as many as 16 loss-based algorithms like Cubic or Reno. In this short paper, we provide the first model capturing BBR’s behavior in competition with loss-based CCAs. Our model is coupled with practical experiments to validate its implications. The key lesson is this: under competition, BBR becomes window-limited by its ‘in-flight cap’ which then determines BBR’s bandwidth consumption. By modeling the value of BBR’s in-flight cap under varying network conditions, we can predict BBR’s throughput when competing against Cubic flows with a median error of 5%, and against Reno with a median of 8%. 

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4. Accurately Parameterizing Internet Performance Testing for Realistic Evaluations

   Philip, Adithya Abraham (August 2025, Carnegie Mellon University)

   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. 

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5. The measure and mismeasure of fairness

   Corbett-Davies, Sam; Gaebler, Johann D.; Nilforoshan, Hamed; Shroff, Ravi; Goel, Sharad (August 2023, Journal of machine learning research)

   Weinberger, Kilian (Ed.)

   The field of fair machine learning aims to ensure that decisions guided by algorithms are equitable. Over the last decade, several formal, mathematical definitions of fairness have gained prominence. Here we first assemble and categorize these definitions into two broad families: (1) those that constrain the effects of decisions on disparities; and (2) those that constrain the effects of legally protected characteristics, like race and gender, on decisions. We then show, analytically and empirically, that both families of definitions typically result in strongly Pareto dominated decision policies. For example, in the case of college admissions, adhering to popular formal conceptions of fairness would simultaneously result in lower student-body diversity and a less academically prepared class, relative to what one could achieve by explicitly tailoring admissions policies to achieve desired outcomes. In this sense, requiring that these fairness definitions hold can, perversely, harm the very groups they were designed to protect. In contrast to axiomatic notions of fairness, we argue that the equitable design of algorithms requires grappling with their context-specific consequences, akin to the equitable design of policy. We conclude by listing several open challenges in fair machine learning and offering strategies to ensure algorithms are better aligned with policy goals. 

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