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.
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Modeling BBR’s Interactions with Loss-Based Congestion Control
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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- Award ID(s):
- 1850384
- NSF-PAR ID:
- 10121285
- Date Published:
- Journal Name:
- Proceedings of the ACM SIGCOMM Internet Measurement Conference
- ISSN:
- 2150-3761
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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BBR is a new congestion control algorithm proposed by Google that builds a model of the network path consisting of its bottleneck bandwidth and RTT to govern its sending rate rather than packet loss (like CUBIC and many other popular congestion control algorithms). Loss-based congestion control has been shown to be vulnerable to acknowledgment manipulation attacks. However, no prior work has investigated how to design such attacks for BBR, nor how effective they are in practice. In this paper we systematically analyze the vulnerability of BBR to acknowledgement manipulation attacks. We create the first detailed BBR finite state machine and a novel algorithm for inferring its current BBR state at runtime by passively observing network traffic.We then adapt and apply a TCP fuzzer to the Linux TCP BBR v1.0 implementation. Our approach generated 30,297 attack strategies, of which 8,859 misled BBR about actual network conditions. From these, we identify 5 classes of attacks causing BBR to send faster, slower or stall. We also found that BBR is immune to acknowledgment burst, division and duplication attacks that were previously shown to be effective against loss-based congestion control such as TCP New Reno.more » « less
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Congestion Control Algorithms (CCAs) impact numerous desirable Internet properties such as performance, stability, and fairness. Hence, the networking community invests substantial effort into studying whether new algorithms are safe for wide-scale deployment. However, operators today are continuously innovating and some deployed CCAs are unpublished - either because the CCA is in beta or because it is considered proprietary. How can the networking community evaluate these new CCAs when their inner workings are unknown? In this paper, we propose 'counterfeit congestion control algorithms' - reverse-engineered implementations derived using program synthesis based on observations of the original implementation. Using the counterfeit (synthesized) CCA implementation, researchers can then evaluate the CCA using controlled empirical testbeds or mathematical analysis, even without access to the original implementation. Our initial prototype, 'Mister 880,' can synthesize several basic CCAs including a simplified Reno using only a few traces.more » « less
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BBR is a newer TCP congestion control algorithm with promising features, but it can often be unfair to existing loss-based congestion-control algorithms. This is because BBR's sending rate is dictated by static parameters that do not adapt well to dynamic and diverse network conditions. In this work, we introduce BBR-ML, an in-kernel ML-based tuning system for BBR, designed to improve fairness when in competition with loss-based congestion control. To build BBR-ML, we discretized the network condition search space and trained a model on 2,500 different network conditions. We then modified BBR to run an in-kernel model to predict network buffer sizes, and then use this prediction for optimal parameter settings. Our preliminary evaluation results show that BBR-ML can improve fairness when in competition with Cubic by up to 30% in some cases.more » « less
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Recent congestion control research has focused on purpose-built algorithms designed for the special needs of specific applications. Often, limited testing before deploying a CCA results in unforeseen and hard-to-debug performance issues due to the complex ways a CCA interacts with other existing CCAs and diverse network environments. We present CC-Fuzz, an automated framework that uses genetic search algorithms to generate adversarial network traces and traffic patterns for stress-testing CCAs. Initial results include CC-Fuzz automatically finding a bug in BBR that causes it to stall permanently, and automatically discovering the well-known low-rate TCP attack, among other things.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3355369.3355604
