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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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This content will become publicly available on October 12, 2026
Uni-MPTCP(⃗ω, n ): a Unified MPTCP Congestion Control Algorithm
A fundamental design principle of MultiPath TCP (MPTCP) congestion control algorithm (CCA) is that an MPTCP flow should be fair to and do not harm TCP flows. Unfortunately, to deal with cost heterogeneity among subflow interfaces, the existing cost-aware MPTCP CCAs often violate this design principle in an attempt to minimize the cost. Based on the network utility maximization (NUM) framework, we put forward Uni-MPTCP(⃗ω, n ), a NUM-optimal, Unified MPTCP CCA with n subflow paths and a n-dimension weight vector⃗ ω with n − 1 independent elements. Uni-MPTCP(⃗ω, n ) abides by this design principle for arbitrary⃗ω and can be customized to achieve specific cost design objectives with proper adaptation of⃗ω . As such, Uni-MPTCP(⃗ω, n ) provides a unified solution to enable cost-aware MPTCP CCAs, while adhering to the design principle. Finally, we put forward an adaptation algorithm for, ω, in Uni-MPTCP(ω, 2), aiming at maintaining a target MPTCP flow rate with minimum cost for a cost-heterogeneity case with dual connectivity. The test results based on NS-3 simulation demonstrate that Uni-MPTCP(ω, 2) can indeed effectively keep track of a given flow rate target with minimum cost, while adhering to the design principle.
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- Award ID(s):
- 2226117
- PAR ID:
- 10636343
- Publisher / Repository:
- IEEE
- Date Published:
- Format(s):
- Medium: X
- Location:
- Sydney, Australia
- Sponsoring Org:
- National Science Foundation
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