[Bloat] when does the CoDel part of fq_codel help in the real world?

Mario Hock mario.hock at kit.edu
Fri Nov 30 04:55:15 EST 2018


Luca,

I'm not that happy with the theorem either, since it stresses a 
limitation that can actually be overcome. However, I quoted it to 
demonstrate there is a challenge involved.

In my point of view there is actually a subtle thing that's often lost 
when modeling the queue based on input rates, output rates, and, e.g., 
queuing theory. The inflight data (e.g., controlled by the CWnds) and 
the resulting standing queue. But this standing queue is (probably) the 
major reasoning behind LoLa and CoDel.

Let's simplify the situation to single sender, to ease the explanation. 
(Also holds for multiple senders.) Let the sender have a CWnd-based 
congestion control, but let's fix this CWnd for now. Also, let's assume 
the CWnd is larger than the BDP (without queuing delay); CWnd = BDP + x.

In this case, the sending rate will be above the bottleneck rate, as 
long as less than x is queued at the bottleneck. This increases the 
queue. As soon as x is queued at the bottleneck, the sending rate will 
(exactly) approach the bottleneck rate. The reason is, that the BDP 
(with buffering) will now be exactly equal to the CWnd (since the RTT is 
increased).

This means, a standing queue will build up. But as soon as it's there, 
sending rate will (on average) be equal to the bottleneck rate.

There is also a stability condition here. If the queuing delay (for some 
reason) falls below x/rate (i.e., amount of queuing falls below x), the 
sending rate increases. If the queuing delay raises above x/rate, 
sending rate is reduced. Note: That this is all with a fixed CWnd. No 
interaction of the congestion control algorithms involved here.

Therefore, there can be a long living standing queue, even if input == 
output. Of course during the build up process input > output, however 
this build up can e.g. happen in one RTT, while the standing queue can 
survive seconds or minutes (in reality, and indefinitely in theory). 
Though, I found that this correlation is often not modeled in 
queue-theoretical models.

Best, Mario


Am 29.11.18 um 23:30 schrieb Luca Muscariello:
> Mario,
> 
> putting aside LoLa for a second,
> I'm not quite sure that the theorem you cite is valid.
> 
> According to the model R_i is the sending rate.
> The sum across all flows bottlenecked at the link does not need to be 
> equal to the link capacity.
> The input rate can be instantaneously below or above the link capacity.
> Even If the link is never idle and the output rate is always C for all t.
> 
> If the sum_i R_i = C is false because of what I said, than p_i which is 
> nothing more than
> the shadow price of the link capacity constraint, can be a function of a 
> constant delay d, i.e. p_i = cost * d for all i.
> 
> This theorem can be valid only if the input rate of a queue is 
> instantaneously equal to the output queue.
> We all know that a queue exists just because there is an instantaneous 
> difference of input and output rates
> at the link. So to conclude,  this theorem if valid iff input == output 
> rate then the queue is always zero, i.e.  d=0.
> 
> The theorem is either an artefact of the model or just wrong. Or I'm 
> missing something...
> 
> 
> 
> 
> 
> 
> On Thu, Nov 29, 2018 at 6:07 PM Mario Hock <mario.hock at kit.edu 
> <mailto:mario.hock at kit.edu>> wrote:
> 
>     Hi Luca,
> 
>     I'm answering on behalf of Roland, since I am a co-author of the paper.
> 
>     This is an excellent question, since it goes right at the heart of how
>     LoLa works.
> 
>     Indeed, the paper is a first of a series. A second one, looking deeper
>     into the fair flow balancing mechanism, is currently under submission.
> 
>     Similar as other delay based congestion controls, LoLa tries to achieve
>     fairness by allowing each flow to buffer the same amount of data at the
>     bottleneck. We have this, e.g., in TCP Vegas, and (in a way) also in
>     Copa (a recently proposed congestion control) and many others. If this
>     is achieved, we get flow rate fairness independent of a flow's RTT.
> 
>     Usually (in other congestion controls) this "allowed amount of data" is
>     fixed per flow. We presume that this approach does not scale well to
>     high speed networks. Since the queuing delay resulting from this amount
>     of data is reduced with increasing bottleneck rate. Thus, it becomes
>     harder to measure it right. This can easily be seen (and proven) for
>     TCP
>     Vegas.
> 
>     Note: Just using higher fixed values is not an option, since it would
>     not work at lower speeds anymore and also not with a large number of
>     flows.
> 
>     Therefore, LoLa tries to find a suitable value for the "allowed amount
>     of data" dynamically. This is X(t).
> 
>     Our approach is to grow X(t) over time during the Fair Flow Balancing
>     phase. This phase ends when the queuing delay reaches 5ms. Thus, (in
>     the
>     ideal case) at the end of Fair Flow Balancing, X(t) is just as large
>     that all flows at bottleneck create a queuing delay of 5ms, and all
>     flows contribute equally to this queue. Hence, flow rate fairness is
>     achieved. (Note that LoLa is designed in a way that t is (almost)
>     synchronized among the competing flows.)
> 
>     Generally, other ways of determining a suitable X(t) are
>     conceivable. In
>     our approach X(t) is a monotonically increasing function, but it is
>     regularly reset as LoLa cycles between its states; i.e., after a
>     queuing
>     delay of 5ms is reached, the queue is drained and everything starts
>     again. (Thus, the timespan where X(t) is monotonically increased is
>     called a "round of fair flow balancing".)
> 
>     This way we can overcome the constraint given in [1]:
> 
>     """
>     THEOREM 6 (FAIRNESS/DELAY TRADEOFF). For congestion control mechanisms
>     that have steady state throughput of the kind R = f(d, p), for some
>     function f, delay d and feedback p, if the feedback is based on purely
>     end to end delay measurements, you can either have fairness or a fixed
>     delay, but not both simultaneously
>     """
> 
>     [1] "ECN or Delay: Lessons Learnt from Analysis of DCQCN and TIMELY"
>     Yibo Zhu et al., https://dl.acm.org/citation.cfm?id=2999593
> 
>     Best, Mario
> 
> 
>     Am 29.11.18 um 17:09 schrieb Luca Muscariello:
>      > Hi Roland,
>      >
>      > It took me quite a lot of time to find this message in the thread...
>      > I read the paper you sent and I guess this is the first of a
>     series as
>      > many things stay uncovered.
>      >
>      > Just a quick question: why is X(t) always increasing with  t?
>      >
>      >
>      > On Tue, Nov 27, 2018 at 11:26 AM Bless, Roland (TM)
>      > <roland.bless at kit.edu <mailto:roland.bless at kit.edu>
>     <mailto:roland.bless at kit.edu <mailto:roland.bless at kit.edu>>> wrote:
>      >
>      >     Hi Luca,
>      >
>      >     Am 27.11.18 um 10:24 schrieb Luca Muscariello:
>      >      > A congestion controlled protocol such as TCP or others,
>     including
>      >     QUIC,
>      >      > LEDBAT and so on
>      >      > need at least the BDP in the transmission queue to get
>     full link
>      >      > efficiency, i.e. the queue never empties out.
>      >
>      >     This is not true. There are congestion control algorithms
>      >     (e.g., TCP LoLa [1] or BBRv2) that can fully utilize the
>     bottleneck link
>      >     capacity without filling the buffer to its maximum capacity.
>     The BDP
>      >     rule of thumb basically stems from the older loss-based
>     congestion
>      >     control variants that profit from the standing queue that
>     they built
>      >     over time when they detect a loss:
>      >     while they back-off and stop sending, the queue keeps the
>     bottleneck
>      >     output busy and you'll not see underutilization of the link.
>     Moreover,
>      >     once you get good loss de-synchronization, the buffer size
>     requirement
>      >     for multiple long-lived flows decreases.
>      >
>      >      > This gives rule of thumbs to size buffers which is also very
>      >     practical
>      >      > and thanks to flow isolation becomes very accurate.
>      >
>      >     The positive effect of buffers is merely their role to absorb
>      >     short-term bursts (i.e., mismatch in arrival and departure rates)
>      >     instead of dropping packets. One does not need a big buffer to
>      >     fully utilize a link (with perfect knowledge you can keep the
>     link
>      >     saturated even without a single packet waiting in the buffer).
>      >     Furthermore, large buffers (e.g., using the BDP rule of thumb)
>      >     are not useful/practical anymore at very high speed such as
>     100 Gbit/s:
>      >     memory is also quite costly at such high speeds...
>      >
>      >     Regards,
>      >       Roland
>      >
>      >     [1] M. Hock, F. Neumeister, M. Zitterbart, R. Bless.
>      >     TCP LoLa: Congestion Control for Low Latencies and High
>     Throughput.
>      >     Local Computer Networks (LCN), 2017 IEEE 42nd Conference on, pp.
>      >     215-218, Singapore, Singapore, October 2017
>      > http://doc.tm.kit.edu/2017-LCN-lola-paper-authors-copy.pdf
>      >
>      >      > Which is:
>      >      >
>      >      > 1) find a way to keep the number of backlogged flows at a
>      >     reasonable value.
>      >      > This largely depends on the minimum fair rate an
>     application may
>      >     need in
>      >      > the long term.
>      >      > We discussed a little bit of available mechanisms to
>     achieve that
>      >     in the
>      >      > literature.
>      >      >
>      >      > 2) fix the largest RTT you want to serve at full
>     utilization and size
>      >      > the buffer using BDP * N_backlogged.
>      >      > Or the other way round: check how much memory you can use
>      >      > in the router/line card/device and for a fixed N, compute
>     the largest
>      >      > RTT you can serve at full utilization.
>      >      >
>      >      > 3) there is still some memory to dimension for sparse flows in
>      >     addition
>      >      > to that, but this is not based on BDP.
>      >      > It is just enough to compute the total utilization of sparse
>      >     flows and
>      >      > use the same simple model Toke has used
>      >      > to compute the (de)prioritization probability.
>      >      >
>      >      > This procedure would allow to size FQ_codel but also SFQ.
>      >      > It would be interesting to compare the two under this
>     buffer sizing.
>      >      > It would also be interesting to compare another mechanism
>     that we
>      >     have
>      >      > mentioned during the defense
>      >      > which is AFD + a sparse flow queue. Which is, BTW, already
>      >     available in
>      >      > Cisco nexus switches for data centres.
>      >      >
>      >      > I think that the the codel part would still provide the
>     ECN feature,
>      >      > that all the others cannot have.
>      >      > However the others, the last one especially can be
>     implemented in
>      >      > silicon with reasonable cost.
>      >
>      >
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