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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aBBRate: Automating BBR Attack Exploration Using a Model-Based Approach
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.
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- Award ID(s):
- 1801546
- NSF-PAR ID:
- 10170301
- Date Published:
- Journal Name:
- The 23rd International Symposium on Research in Attacks, Intrusions and Defenses (RAID 2020)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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