[Bloat] pacing, applied differently than bbr

[Taran Lynn]

Here's a paper and slides on work that has built on this research
[1][2]. They were presented at the 2019 Buffer Workshop. A paper should
also be posted on arXiv soon that has more details of the actual
algorithm, which has been slightly updated since the workshop. Currently
we're trying to improve the algorithm's performance and fairness. So far
we've seen pretty good reductions in RTT (hopefully you'll see more
papers in the future). We're also learning some things from BBR and the
challenges it faced.

P.S. If you're wondering why the math looks significantly different than
in the original paper, it's because a lot of progress has already been
made :).

[1] http://buffer-workshop.stanford.edu/papers/paper14.pdf
[2] http://buffer-workshop.stanford.edu/slides/mpc.pdf

On 2/8/20 11:11 PM, Dave Taht wrote:
> I don't know how I stumbled across this, but it seemed interesting at
> this late hour. I wonder if they kept at it or tried ecn also.
> 
> "A Model Predictive Control Approach to Flow Pacing for TCP"
> 
> "we propose a different approach to latency based congestion control.
> In particular, our controller sets the maximum pacing rate by solving
> a model-based receding horizon control problem at each time step. Each
> new roundtrip time (RTT) measurement is first incorporated into a
> linear time-varying (LTV) predictive model. Subsequently, we solve a
> one-step look-ahead optimization problem which finds the pacing rate
> which optimally trades off RTT, RTT variance, and throughput according
> to the most recent model. Our method is computationally inexpensive
> making it readily implementable on current systems."
> 
> https://people.eecs.berkeley.edu/~dfk/pdfs/network_control_camera_ready.pdf
> 
>