With the rise of heterogeneous congestion control algorithms and increasingly complex application control loops (e.g. adaptive bitrate algorithms), the Internet community has expressed growing concern that network bandwidth allocations are unfairly skewed, and that some Internet services are 'winners' at the expense of 'losing' services when competing over shared bottlenecks. In this paper, we provide the first study of fairness between live, end-to-end services with distinct workloads. Rather than focusing on individual components of an application stack (e.g., studying the fairness of an individual congestion control algorithm), we want to provide a direct study over real-world deployed applications. Among our findings, we observe that services typically achieve less-than-fair outcomes: on average, the 'losing' service achieves only 72% of its max-min fair share of link bandwidth. We also find that some services are significantly more contentious than others: for example, one popular file distribution service causes competing applications to obtain as low as 16% of their max-min fair share of bandwidth when competing in a moderately-constrained setting.

We present CCAnalyzer, a novel classifier for deployed Internet congestion control algorithms (CCAs) which is more accurate, more generalizable, and more human-interpretable than prior classifiers. CCAnalyzer requires no knowledge of the underlying CCA algorithms, and it can identify when a CCA is novel - i.e. not in the training set. Furthermore, CCAnalyzer can cluster together servers it believes use the same novel/unknown algorithm. CCAnalyzer correctly identifies all 15 of the default Internet CCAs deployed with Linux, including BBRv1, which no existing classifier can do. Finally, CCAnalyzer can classify server CCAs while being as efficient or better than prior approaches in terms of bytes transferred and runtime. We conduct a measurement study using CCAnalyzer measuring the CCA for 5000+ websites. We find widespread deployment of BBRv1 at large CDNs, and demonstrate how our clustering technique can detect deployments of new algorithms as it discovers BBRv3 although BBRv3 is not in its training set.

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?

Today, we depend on numerous large-scale services for basic operations such as email. These services, built on the basis of Continuous Integration/Continuous Deployment (CI/CD) processes, are extremely dynamic: developers continuously commit code and introduce new features, functionality and fixes. Hundreds of commits may enter the code-base in a single day. Therefore one of the most time-critical, yet resource-intensive tasks towards ensuring code-quality is effectively testing such large code-bases. This paper presents FastLane, a system that performs data-driven test minimization. FastLane uses light-weight machine-learning models built upon a rich history of test and commit logs to predict test outcomes. Tests for which we predict outcomes need not be explicitly run, thereby saving us precious test-time and resources. Our evaluation on a large-scale email and collaboration platform service shows that our techniques can save 18.04%, i.e., almost a fifth of test-time while obtaining a test outcome accuracy of 99.99%.

Instructors and learners in every country have increasing access to smartphones, and a variety of pedagogical tools and techniques have been developed to leverage the affordances of these devices in classroom settings. Two common patterns of communication between such devices are many-to-one (e.g., when each learner's device sends data to the instructor's device) and one-to-many (e.g., when the instructor's device sends data to each learner's device). Every classroom usually requires additional infrastructure (such as a Wi-Fi router) to reliably transmit this data, which can be challenging for institutions with limited resources. We therefore present BeaconNet, a hybrid Bluetooth/Wi-Fi beacon-based smartphone ad-hoc network that requires no infrastructure beyond commodity smartphones already owned by learners and instructors. BeaconNet supports many-to-one and one-to-many communication between these devices. For applications where the volume of communication is small (a few tens of bytes per message), it is faster and scales to large classrooms (up to 80 learners) more reliably than existing infrastructure-less solutions.