Re: [tcpm] alpha_cubic (was: Concluding WGLC for draft-ietf-tcpm-rfc8312bis-03)

Yoshifumi Nishida <nsd.ietf@gmail.com> Wed, 08 September 2021 09:04 UTC

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References: <CAAK044SjMmBnO8xdn2ogWMZTcecXoET1dmZqd6Dt3WzOUi359A@mail.gmail.com> <alpine.DEB.2.21.2108300740560.5845@hp8x-60.cs.helsinki.fi> <ccfc4dc2-0570-1ba2-66a5-b5e199f11359@bobbriscoe.net>
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From: Yoshifumi Nishida <nsd.ietf@gmail.com>
Date: Wed, 8 Sep 2021 02:04:05 -0700
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To: Bob Briscoe <ietf@bobbriscoe.net>
Cc: "tcpm@ietf.org Extensions" <tcpm@ietf.org>
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Subject: Re: [tcpm] alpha_cubic (was: Concluding WGLC for draft-ietf-tcpm-rfc8312bis-03)
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Hi Bob,

Thank you so much! I will think it through.
--
Yoshi

On Tue, Sep 7, 2021 at 5:17 AM Bob Briscoe <ietf@bobbriscoe.net> wrote:

> Yoshi,
>
> You recently asked Markku for an explanation of his point #4.
>
> Coincidentally, a few weeks ago, I was checking the formula in RFC8312 for
> Cubic in TCP-Friendly mode (called "C-Reno" in this email for brevity), and
> I also checked back on that preliminary paper that Markku refers to. I
> think Markku has a different way of explaining the flaw in the equation,
> but below I'll try to explain what I think the problems are. See [BB]
> inline...
>
> If you don't read HTML email, I'm afraid the maths is probably going to
> look like gobbledygook.
>
> On 30/08/2021 17:33, Markku Kojo wrote:
>
> Hi Yoshi, all,
>
> On Wed, 18 Aug 2021, Yoshifumi Nishida wrote:
>
> 4. Fairness to AIMD congestion control
>
>    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.
>
>
> [BB] The source of eqn (4) in RFC8312 is eqn (4) in [FHP00]. This paper
> has been cited 250 times but it is not peer reviewed.
>
> Equations (2) & (3) of [FHP00] that lead up to (4) use an RTT (R) as if it
> doesn't vary. But it does, because the sawteeth vary the queue. But the
> real problem is that the model (silently) assumes that R is an average RTT
> sitting part-way up the sawteeth, whatever the link rate or base RTT. This
> overlooks the fact that, if the tips of the sawteeth are always at the same
> queue depth (as in a tail-drop buffer) the average R will be higher if the
> sawteeth have smaller amplitude. This sounds picky, but the assumption on
> whether sawteeth vary about their bottom, top or middle greatly alters the
> outcome:
>
> I've taken three possible alternative assumptions, with their resulting
> additive increase factors for theoretical equality with Reno (I've given
> all the derivations at the end of this email):
> #1) All sawteeth have the same Rmin: α ~= (1/β2 - 1) / 3;        Example:
> β = 0.7; α ~= 0.35
> #2) All sawteeth have the same Rmax: α ~= 4(1-β2) / 3;
> Example: β = 0.7; α ~= 0.68
> #3) All sawteeth have the same Ravg: α ~= 3(1-β) / (1+β);        Example:
> β = 0.7; α ~= 0.53    <== used in [FHP00] and therefore in RFC8312
>
>
> Which assumption is most applicable?
> #2 models a tail-drop queue.
> #3 (or somewhere between #3 and #2) models a probabilistic AQM, such as
> PIE or RED.
> #1 is not applicable to the current Internet, but it would be if a magic
> AQM was invented that minimized the standing queue.
> I don't think any of the assumptions are good models of C-Reno's
> interaction with CoDel.
>
> So which one should draft-ietf-tcpm-rfc8312bis recommend?
> You might think #2, given one suspects the vast majority of queues are
> still tail drop...
> However, it may be 'none of the above', 'cos the paper also says it
> assumes deterministic marking; i.e. that that each flow experiences just
> one drop or mark at the top of each sawtooth. If that applies at all, it
> only applies to AQMs, not to tail-drop buffers that tend to drop more than
> one packet at a time. Nonetheless, for the purpose of rate comparison, all
> the flows will share the same bottleneck. So if flow rates are equal under
> a certain AQM, they might be equal under tail-drop too (see {Note 1} at the
> very end).
>
> I would still like to say 'none of the above' because Linux C-Reno has
> been widely used with α=1, β=0.7 for many years now without the sky
> falling. So it's questionable whether friendliness should be defined
> relative to Reno. Otherwise, from day 1, RFC8312bis will be stating the
> forlorn hope that Linux C-Reno should regress to become less aggressive
> than its former self, just to be friendly to Reno, which apparently barely
> exists any more (according to the Great TCP Congestion Control Census of
> 2019 [MSJ+19]).
>
> Nonetheless, we need to bear in mind that BSD variants of Cubic (e.g.
> Apple's) are also widely used and I believe they comply with the α for
> C-Reno defined in RFC8312.
>
> (FAQ: Why does C-Reno add <1 segment per RTT to be friendly to Reno, which
> adds 1 segment per RTT?
> C-Reno in the RFC multiplicatively decreases cwnd to 0.7*cwnd on a loss
> (multiplicative decrease factor β=0.7), whereas Reno uses β=0.5. So,
> because C-Reno reduces less, it's also meant to increase less per round so
> that the sawteeth don't reach the max window again more quickly than Reno
> would. That is, it's meant to use a smaller AI factor (α) than 1 segment.
> Using α<1 doesn't necessarily slow down Cubic's acceleration into unused
> capacity, because it can switch to a convex (hockey stick) Cubic curve once
> its slow linear increase has made the queue large enough to come out of its
> 'TCP-Friendly' mode.)
>
> Whatever, rfc8312bis certainly shouldn't refer solely to [FHP00] without
> caveats.
>
> Then we have the problem that none of the above formulae are verified
> against reality.
> Even in the [FHP00] paper, which uses assumption #3, the simulations are
> out by about 2x. The paper used β=7/8. So, from the theory, for each of the
> above three assumptions, the additive increase factor would have had to be:
> α1 = 0.10
> α2 = 0.31
> α3 = 0.20
> They used a RED AQM (which should be somewhere between assumption #2 & #3).
> But they had to configure C-Reno with α=0.4 to get close to equal flow
> rates with Reno
> Using my 'finger in the air' modification to the formulae in {Note 1}, α3
> = sqrt(0.20) = 0.44, which would have been about right (perhaps only
> coincidentally).
>
> The question over what α to put in the RFC could run and run. So here's a
> suggested path through this quagmire:
> These alternatives throw doubt on the wisdom of using assumption#3. So we
> could at least pick a new assumption for a theoretical α. Assumption#2 is
> probably the 'most correct', and also happens to give the highest value of
> α for the 'TCP-Friendly' region (α=0.68 when β=0.7).
> (By my unsubstantiated formula in {Note 1}, α2 = sqrt(0.68) = 0.82.)
>
> If we look for an empirical value of α that gives reasonably equal flow
> rates against Reno, I suspect we will get all sorts of different answers
> depending on the conditions, e.g. AQM or not; high or low multiplexing;
> high or low RTT (shared by all flows); pacing, burstiness, etc,etc. If you
> want to take this path, feel free, but if you're still on it in a year's
> time, don't say I didn't warn you.
>
>
> Derivations of the above three formulae follow at the end.
>
> 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.
>
> /Markku
>
>
> [BB] Derivations of the formulae for α under the three different
> assumptions follow. It's possible/likely that this is all already in a
> paper somewhere, but I haven't been able to find one.
> 0) Preparatory material
> Terminology:
> The subscript for C-Reno is omitted given it would be on every term.
> The recovery time of an AIMD congestion control is the average time for
> additive increase to recover the window reduction after a multiplicative
> decrease.
>
> The overall approach is:
> A) Find a formula in terms of the AI and MD factors (α & β), for the
> recovery time of C-Reno.
> B) Equate this to the recovery time of Reno, which will produce a formula
> for the additive increase factor, α.
>
> The recovery time formula for C-Reno is derived by:
> 1) summing up all the round trips in additive increase, which become
> increasingly large as the queue grows. This sum is stated in terms of the
> number of rounds per cycle, J.
> 2) Then J is found by writing two formulae;
>   2a) one for the average sawtooth decrease
>   2b) and the other for the increase.
> Then by assuming a steady state the two can be equated.
> Here goes...
>
> The following formula gives the recovery time, Tr, from one max window to
> the next, in C-Reno with:
> * additive increase (AI) factor α [segment / RTT]
> * and packet-rate r [packet / s]
> assuming that the minimum window of each sawtooth still fully utilizes the
> link.
>     Tr = ΣJ-1j=0    (Rmin + jα/r)
> where j is the index of each round, and J is the number of round trips of
> additive increase per sawtooth. The rationale for the second term is that
> the queue grows by α fractional packets in each round, and the queue delay
> per packet is 1/r (seconds per packet is reciprocal of packets per second).
> The sum of all the second terms forms an arithmetic progression, the sum of
> which is given by the well-known formula, as follows:
>          = J.Rmin + J(J-1)α/2r
>        ~= J.Rmin + J2α/2r.                                (1)
> The approximation holds as long as J>>1. In some implementations (e.g.
> Linux), cwnd increases continuously, so strictly the limits should be j=1/2
> to J-1/2 (the average half way through the first and last rounds), but
> details like this are insignificant compared to the approximation.
>
> The difference in RTT due to the additional queue delay between the top
> and bottom of the sawtooth can be stated in two ways.
> Either as the sum of all the queue delays of each additive increase:
>     (Rmax - Rmin) = Jα/r,                                (2a)
> Or by the multiplicative relationship between the max and min RTTs, by the
> definition of β: Rmin = β.Rmax.
>     (Rmax - Rmin) = (Rmin / β) - Rmin
>                                = Rmin(1-β)/β                 (2b)
> Equating (2a) and (2b):
>     J = r.Rmin(1-β) / (αβ)                                  (2)
>
> Substituting for J from (2) in (1):
>     Tr = r.Rmin2 (1-β) / (βα) + r.Rmin2 (1-β)2/(2β2α)
>          = r.Rmin2 (1-β) / (βα) * (1 + (1-β)/2β )
>          = r.Rmin2 (1-β)(1+β) / (2β2α)
>          = r.Rmin2 (1-β2) / (2β2α)                                     (3)
> And, because Rmin = β.Rmax.
>     Tr = r.Rmax2 (1-β2) / (2α)                                         (4)
>
> The well-known steady-state Reno formula from [FF99] is:
>     rReno ~= (1/Ravg) sqrt(3 / 2p)
>             (5)
> assuming deterministic marking (which is also assumed for the above model
> of C-Reno).
>
> Assuming a Reno sawtooth is approximately linear, the average RTT of a
> Reno flow,
>     Ravg ~= (Rmax + Rmin)/2
> and
>     Rmin = Rmax/2
> Therefore
>     rReno ~= (1/Rmin) sqrt(2 / 3p)
>             (6)
>     rReno ~= (1/Rmax) sqrt(8 / 3p)
>             (7)
>
> With the preparatory material done, next we take each of the three
> assumptions, one at a time.
> #1) All sawteeth have the same Rmin
> From Eqn (3):
>     Tr = r.Rmin2 (1-β2) / (2β2α)
> Total packets sent over period Tr is r.Tr during which time assume 1
> packet is lost (or marked) at the sawtooth peak {Note 1}. So loss
> probability:
>     p = 1 / r.Tr
> Substituting for Tr and rearranging :
>     r2 = 2β2α / Rmin2 (1-β2)p
>     r = (β/Rmin) sqrt(2α / (1-β2)p)
> Equating the Reno packet rate, rReno, from eqn (6) to the C-Reno packet
> rate, r, for all α and β:
>     r = rReno
>        = (1/Rmin) sqrt(2 / 3p).
> Therefore,
>     (1/Rmin) sqrt(2 / 3p) ~= (β/Rmin) sqrt(2α / (1-β2)p)
> Rearranging
>     2/3 ~= 2αβ2 / (1-β2)
>     α ~= (1-β2)/3β2
>        ~= (1/β2 - 1) / 3
>                                 (8)
> #2) All sawteeth have the same Rmax = Rmin / β
> From Eqn (4):
>     Tr = r.Rmax2 (1-β2) / (2α)
> As before, after substituting for Tr and rearranging:
>     p = 1 / r.Tr
>     r = (1/Rmax) sqrt(2α / (1-β2)p)
> Equating to rReno from eqn (7) for all α and β,
>       ~= (1/Rmax) sqrt(8 / 3p).
> Rearranging
>     8/3 ~= 2α / (1-β2)
>     α ~= 4(1-β2) / 3
>                             (9)
>
> #3) All sawteeth have the same Ravg Ravg = (Rmax + βRmax )/2
>          = Rmax (1+β)/2
> From Eqn (4):
>     Tr = r.Rmax2 (1-β2) / (2α)
>          = 2r.Ravg2 (1-β) / α(1+β)
> As before, after substituting for Tr and rearranging:
>     p = 1 / r.Tr
>     r = (1/Ravg) sqrt(α (1+β) / 2(1-β)p)
> Equating to rReno from eqn (5) for all α and β,
>       ~= (1/Ravg) sqrt(3 / 2p).
> Rearranging
>     3/2 ~= α(1+β) / 2(1-β)
>     α ~= 3(1-β) / (1+β)
>                         (10)
>
> This last formula is same as the equation used for C-Reno in RFC8312 and
> in the RFC8312bis draft.
>
>
> {Note 1}: The assumption in [FHP00] of deterministic marking is suspect
> for the Internet (meaning that the spacing between drops or marks is even
> and there is always 1 packet dropped or marked at the top of each sawtooth).
> The actual average dropped (or marked) per sawtooth will depend on whether
> the buffer uses tail-drop or an AQM, and if so which AQM. It is perhaps
> better not to assume the actual average number dropped (or marked) is
> known, but instead assume the ratio of the average drops or marks between C-Reno
> and Reno will be roughly proportional to their AI factors. This would
> modify the formula for C-Reno's drop probability to:
>     p = αReno / αC-Reno.r.Tr
> Substituting αReno = 1:
>     p = 1 / αC-Reno.r.Tr
> This would modify all the results to:
>
> #1) All sawteeth have the same Rmin: α ~= sqrt( (1/β2 - 1) / 3 );
> Example: β = 0.7; α ~= 0.59
> #2) All sawteeth have the same Rmax: α ~= sqrt( 4(1-β2) / 3 );
> Example: β = 0.7; α ~= 0.82
> #3) All sawteeth have the same Ravg: α ~= sqrt( 3(1-β) / (1+β) );
> Example: β = 0.7; α ~= 0.73
>
> However, these would then be wrong where there really is deterministic
> marking.
>
> References
> =========
> [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>
>
> [MSJ+19] Ayush Mishra, Xiangpeng Sun, Atishya Jain, Sameer Pande, Raj
> Joshi, and Ben Leong. The Great Internet TCP Congestion Control Census. Proc.
> ACM on Measurement and Analysis of Computing Systems, 3(3), December 2019
>
> [FF99] Sally Floyd and Kevin Fall, "Promoting the Use of End-to-End
> Congestion Control in the Internet", IEEE/ACM ToN (1999)
>
>
> Bob
>
> --
> ________________________________________________________________
> Bob Briscoe                               http://bobbriscoe.net/
>
>