[Bloat] pacing, applied differently than bbr

[Dave Taht]

A couple thoughts:

"the scheme perform poorly in networks that experience high levels of
random losses.. Such networks include most all networks with WiFi
access points, which make up an increasing number of access points."

While not exactly a myth, most wifi networks today actually do an
extreme level of hardware retry to obscure losses, some retrying 30 or
more times before giving up (on unicast). So you are more likely to
see extremes of RTT variance than actual loss on such networks (until
the introduction of intentional loss or marking from the fq_codel for
wifi stuff). Loss and RTT variance could perhaps be better correlated,
and I've sometimes had hope people would take that RTT variance into
account when dealing with loss (and especially ecn!) response to
"rate" reduction. There really is no such thing as a "rate" when
dealing with wifi, it's just data over an interval, where that
interval varies more than how most humans think of the thing, and
there's lots of different intervals, including fairly large ones where
the rate can be *0*. I try to give some intuition in the latter half
of my old mit talk here:

https://www.youtube.com/watch?v=Wksh2DPHCD

As to how mmwave radios will actually behave in the field, not a clue.

"One notable advantage of FQ over FQ Codel is that provides flow
pacing to applications and higher-level congestion control algorithms.
This is why FQ is suggested to be used with BBR and not FQ Codel." -
this is not quite true. The FQ qdisc is *more efficient* at managing
the pacing itself, but since linux 4.something, the tcp stack will
fall back to keep the timers and pacing within itself for any qdisc.
I'm always at pains to praise the sch_fq qdisc - really, it's awesome
stuff for a tcp-serving-heavy workload on bare metal, but for any
other workload...

And pacing helps cubic and reno a lot as well.

As for the actual content of the paper... umm.. code is where? One of
the things opening up to the bpf and related approaches is perhaps
better means for couple congestion control (e.g. multiple ledbat-like
flows), and I like very much maybe having a knob for other sorts of
flows....

On Tue, Feb 25, 2020 at 10:52 PM Taran Lynn <taranlynn0 at gmail.com> wrote:
>
> As promised, here's the updated arXiv paper on applying model predictive
> control to TCP CC [1]. It contains more in depth information about the
> implementation, as well as some data from physical experiments.
>
> [1] https://arxiv.org/abs/2002.09825
>
> On 2/9/20 8:39 AM, Taran Lynn wrote:
> > 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
> >>
> >>
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Dave Täht
CTO, TekLibre, LLC
http://www.teklibre.com
Tel: 1-831-435-0729