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This repository has been archived by the owner on Aug 28, 2023. It is now read-only.
The equation on page 12 to derive increase factor α_cubic that
intends to achieve the same average window as AIMD TCP seems to
have its origins in a preliminary paper that states that the
authors do not have an explanation to the discrepancy between
their AIMD model and experimental results, which clearly deviate.
It seems to have gone unnoticed that the equation assumes equal
drop probability for the different values of the increase factor
and multiplicative decrease factor but the drop probability
changes when these factors change. The equations for the drop
probability / the # of packets in one congestion epoch
are available in the original paper and one can easily verify
this. Therefore, the equations used in CUBIC are not correct
and seem to underestimate W_est for AIMD TCP, resulting in
moving away from AIMD-Friendly region too early. This gives
CUBIC unjustified advantage over AIMD TCP particularly in
environments with low level of statistical multiplexing. With
high level of multiplexing, drop probability goes higher and
differences in the drop probablilities tend to get small. On the
other hand, with such high level of competition, the theoretical
equations may not be that valid anymore.
The text was updated successfully, but these errors were encountered:
Thanks, Markku! I agree with you that the AIMD model presented in [FHP00] and adopted by Cubic is only an approximate model (e.g., assumption of packet loss). Cubic chooses this model to calculate alpha_cubic, because it is simple and thus easier to implement and captures some basic behaviors of AIMD algorithms. If there are other more accurate and easy-to-implement AIMD models, Cubic should be updated to adopt these models.
[FHP00] Floyd, S., Handley, M., and J. Padhye, "A Comparison of Equation-Based and AIMD Congestion Control", May 2000, https://www.icir.org/tfrc/aimd.pdf.
Markku Kojo said,
The text was updated successfully, but these errors were encountered: