Subject: Re: [aqm] CoDel's control law that determines drop frequency

From: Bob Briscoe <[email-masked]>
Date: Thu, 1 Oct 2015 14:06:42 +0100

Polina,

I've answered your points but changed their order...

On 30/09/15 16:43, Polina Goltsman wrote:
> Bob,
>
> If I understand Codel's law correctly, Codel "starts fresh" every time 
> it enters dropping state, so when the load increases it will take more 
> time for the control law to reach the correct "count" value for the 
> queue to drop. Thus with higher load latency is increased.
As Jeff has said, Codel has been modified to not start count fresh every 
time it enters dropping state.

My point was that no-one has questioned the control law itself, once in 
dropping state. All the activity seems to have been around avoiding 
having to start increasing count from fresh. However, the rate that the 
control law increases count is completely disconnected from how bad the 
queue is getting. Any good control system should make the strength of 
the correction depend on how far the performance metric (queue delay) is 
from its target.
>
> BTW, I haven't seen any place in the original specification that 
> suggested that fixed target delay is the intended design goal.

'target' is the fixed delay target. The whole point of CoDel is to 
detect when queue delay exceeds this target for more than interval, then 
bring it back to this target by dropping packets.

>
> Now, if I understood your curvey red report correctly, you argued that 
> AQM should increase latency when load increases since otherwise it 
> will cause too much loss. Which makes Codel's behavior at least 
> justified ...
No. At higher load CoDel's control law behaviour does not aim for a 
higher target delay. It still aims for 'target'. In this thread so far, 
we have been talking about sluggish dynamic behaviour in reaching the 
target, not the target itself.

Just because a journey to the wrong place happens to go through the 
right place, doesn't justify wandering slowly on the way to the wrong 
place. Admittedly, you will be near the right place for a little longer, 
but you'll also be in all the wrong places for longer, and once you 
reach your destination, you will stay in the wrong place.

> May I ask how curvy red is supposed to perform in those situations?
>
Like CoDel, Curvy RED has a) a target and b) a process for getting there.

a) Unlike CoDel, the target delay is not fixed, it increases a little 
with load. As you say, this avoids having to introduce too much loss. 
The precise compromise between the two depends on how strongly each of 
loss and delay affect the performance of typical applications - there is 
not one answer to that, but I'm working on finding a reasonable compromise.

b) Like any good AQM, Curvy RED doesn't jump straight to its target. We 
have introduced some smoothing delay so it doesn't start dropping 
packets too quickly when hit by a burst that might disappear. Initially 
we just used the same approach as RED - using an exponentially weighted 
moving average of the queue. It works OK. We could probably improve on 
this smoothing. But, as long as Curvy RED isn't significantly worse than 
other AQMs, my main focus is the L4S side of the DualQ AQM that Koen 
presented. I'm happy for others to improve on Curvy RED for existing TCP 
traffic if they want - I won't get round to that for a while.

CoDel's (fixed) interval addresses this burst-smoothing problem, and 
CoDel's (fixed) control law adds to its smoothing delay. It's unclear to 
me why CoDel uses this control law to find the right level of drop. 
Hence my question to Kathy & Van back in 2013 that started this thread 
and still hasn't been answered.

Cheers

Bob

> Does this make any sense?
>
> Regards,
> Polina
>
> On 09/30/2015 02:59 PM, Bob Briscoe wrote:
>> Polina,
>>
>> I think this was it:
>> <https://www.ietf.org/proceedings/85/slides/slides-85-iccrg-2.pdf>
>>
>> I have a set of charts from Rong with many more tests showing CoDel's 
>> sluggish responsiveness, but I believe the above was the published 
>> summary.
>>
>>
>> Bob
>>
>> On 30/09/15 10:13, Polina Goltsman wrote:
>>> Dear Bob,
>>>
>>> On 09/30/2015 10:50 AM, Bob Briscoe wrote:
>>>>
>>>> Early on, Rong Pan showed that it takes CoDel ages to bring high 
>>>> load under control. I think this linear increase is the reason.
>>>
>>> Is there a link to this ?
>>>
>>> Polina
>>>
>>> _______________________________________________
>>> aqm mailing list
>>> [email-masked]
>>> https://www.ietf.org/mailman/listinfo/aqm
>>
>
> _______________________________________________
> aqm mailing list
> [email-masked]
> https://www.ietf.org/mailman/listinfo/aqm

-- 
________________________________________________________________
Bob Briscoe                               http://bobbriscoe.net/

---

From: Jeff Weeks <[email-masked]>
Date: Thu, 22 Oct 2015 17:28:10 +0000

Getting back to this...

Bob; I agree.  Seems that the further we are from the target, the coarser the adjustments should be towards the target (and therefore, in codel, the larger increments of count, leading to larger reduction of the interval).

One problem is large modifications of 'count' in codel could result in the integer based calculation of 1/sqrt(count), that most implementation use, to lose accuracy, and may actually result in it diverging *away* from the correct value, rather than moving towards it.

I've seen success using CAKE's model of halving count upon re-entering the drop state (as well as multiplying 1/sqrt(count) by sqrt(2), for parity, but as you've noted, this just modifies the starting position -- the approach to the correct value is still a steady linear walk, and is unaffected by how far from the target we are.

It seems like codel could benefit by modulating 'count' based on some factor of the difference between the current packet latency, and the desired packet latency (i.e., the target), but again, that would make pre-calculating 1/sqrt(count) more challenging, and it's a non-starter (at least for me) to *actually* have to calculate with the full division and sqrt.

Cheers,
Jeff
________________________________________
From: aqm [[email-masked]] on behalf of Bob Briscoe [[email-masked]]
Sent: Thursday, October 01, 2015 9:06 AM
To: Polina Goltsman
Cc: AQM IETF list
Subject: Re: [aqm] CoDel's control law that determines drop frequency

Polina,

Cheers

Bob

---

From: Dave Dolson <[email-masked]>
Date: Thu, 22 Oct 2015 20:07:42 +0000

Catching up...

Bob said:
> I meant to say in my mail to Andrew that the rate at which the control 
> law increases the dropping frequency ought to depend on how far the 
> queue is from target, or even how quickly it is diverging from target. 
> I've always said that this control law should not be just a hard-coded 
> thing that has no dependency on how the queue is moving.

In other words, make it a PID controller, not just a PI controller?
(Or is it currently just "I" ?)
With consideration of the derivative term (how quickly the queue 
size is moving away from the target), a faster deviation from target 
queue size would cause a faster response.

I find it unfortunate that neither CODEL nor PIE algorithms are formulated as a
controller. It's difficult to reason about.
(They *are* controllers, just not explained that way.)
IMHO, an explanation should begin with a linear controller,
with drop probability as a function of queue size,
and then explanations of why non-linear modifications were necessary.

-----Original Message-----
From: aqm [mailto:[email-masked]] On Behalf Of Bob Briscoe
Sent: Wednesday, September 30, 2015 9:09 AM
To: Dave Taht; Andrew Mcgregor
Cc: Kathleen Nichols; AQM IETF list; Van Jacobson
Subject: Re: [aqm] CoDel's control law that determines drop frequency

Dave,

It says the point at which cake enters drop state depends on how quickly 
the queue is growing.
But I don't see anything on the page you referenced about a better 
curve. I looked under AQM and under other possibly relevant headings.

I meant to say in my mail to Andrew that the rate at which the control 
law increases the dropping frequency ought to depend on how far the 
queue is from target, or even how quickly it is diverging from target. 
I've always said that this control law should not be just a hard-coded 
thing that has no dependency on how the queue is moving.

Also below I've restated a couple of points I made back in 2013 when I 
first posted this.

On 12/11/13 22:30, Bob Briscoe wrote:
> 5/ My analysis also shows that the rate of increase in drop probablity 
> is inversely proportional to the link rate. I don't believe this is 
> desirable, as it will require tuning for the expected number of flows 
> on the link. However it is probably OK for variable rate links.
>
> 6/ The rate of increase of drop probability is also inversely 
> proportional to the square of 'interval'. I suggest a new constant is 
> defined for the control law, rather than relating the control law to 
> interval in this way. Otherwise, when anyone tunes interval, they will 
> get undesirable changes to the control law (e.g. reducing interval 10x 
> [to 10ms] would make the control law 100x more sluggish).

This last point is particularly important. If the control law is still 
to be hard-coded, it should relate to another variable RTT_ave, not 
interval. TO decide when to enter dropping state, interval should be set 
to the likely worst-case RTT (in the low hundreds of ms). But, If 
hard-coded, the dynamics of the control law will be correct most often 
if it depends on the /most likely/ RTT, which these days with CDNs is 
perhaps 10-25ms (depending on population density in a country).

There's another reason for having two separate variables, not just 
'interval' for both. I've said the following before somewhere as well: 
For ECN marking, 'interval' can be set much lower (or even zero) because 
being on a hair-trigger with ECN marks doesn't matter, whereas it does 
for drops.{Note 1} Then CoDel will signal ECN without the sluggish delay 
it has to have before it starts dropping. However, once it has entered 
dropping state, the ECN control law still needs to be based on the same 
RTT_ave as for drop, because the control law is trying to mimic the 
dynamics of the congestion control, which RFC3168 says should be the 
same for ECN as for drop.{Note 2}

Bob

{Note 1}: The faster dynamics won't affect the long-term stable rate 
between an ECN and a non-ECN flow, because by definition long-term 
stable means the dynamics have settled.
{Note 2}: As suggested in draft-briscoe-aqm-dualq-coupled-00, if the 
congestion control were radically different, the packets would probably 
need another identifier to distinguish them from 'classic' ECN.

On 30/09/15 10:00, Dave Taht wrote:
> On Wed, Sep 30, 2015 at 1:50 AM, Bob Briscoe <[email-masked]> wrote:
>> Andrew,
>>
>> I am also not so interested in an AQM dealing directly with unresponsive
>> traffic - I prefer to keep policing and AQM as separately deployable
>> functions, because AQM should be policy-neutral, whereas policing inherently
>> involves policy.
>>
>> My concern was merely that CoDel's linear increase in drop probability can
>> take a long time to reach where it intends to get to. I would have thought
>> some form of exponential increase, or at least super-linear, would have been
>> more responsive to changing traffic conditions. I.e., rather than have to
>> answer the question "how quickly should drop probability increase?", make it
>> increase increasingly quickly.
>>
>> Early on, Rong Pan showed that it takes CoDel ages to bring high load under
>> control. I think this linear increase is the reason.
> cake uses a better curve for codel, but we still need to do more
> testing in the lab.
>
> http://www.bufferbloat.net/projects/codel/wiki/CakeTechnical
>
>> Bob
>>
>>
>>
>> On 30/09/15 01:42, Andrew Mcgregor wrote:
>>
>> Hmm, that's really interesting.
>>
>> Most interesting is that my understanding is that the control law was
>> intended to deal with aggregates of mostly TCP-like traffic, and that an
>> overload of unresponsive traffic wasn't much of a goal; this seems like
>> vaguely reasonable behaviour, I suppose, given that pathological situation.
>>
>> But I don't have a way to derive the control law from first principles at
>> this time (I haven't been working on that for a long time now).
>>
>> On 25 September 2015 at 06:27, Bob Briscoe <[email-masked]> wrote:
>>> Toke,
>>>
>>> Having originally whinged that no-one ever responded to my original 2013
>>> posting, now it's my turn to be embarrassed for having missed your
>>> interesting response for over 3 months.
>>>
>>> Cool that the analysis proves correct in practice - always nice.
>>>
>>> The question is still open whether this was the intention, and if so why
>>> this particular control law was intended.
>>> I would rather we started from a statement of what the control law ought
>>> to do, then derive it.
>>>
>>> Andrew McGregor said he would have a go at this question some time ago...
>>> Andrew?
>>>
>>>
>>> Bob
>>>
>>>
>>>
>>> On 07/06/15 20:27, Toke Høiland-Jørgensen wrote:
>>>
>>> Hi Bob
>>>
>>> Apologies for reviving this ancient thread; been meaning to get around
>>> to it sooner, but well... better late than never I suppose.
>>>
>>> (Web link to your original mail, in case Message-ID referencing breaks:
>>> https://www.ietf.org/mail-archive/web/aqm/current/msg00376.html ).
>>>
>>> Having recently had a need to understand CoDel's behaviour in more
>>> detail, your analysis popped out of wherever it's been hiding in the
>>> back of my mind and presented itself as maybe a good place to start. :)
>>>
>>> So anyhow, I'm going to skip the initial assertions in your email and
>>> focus on the analysis:
>>>
>>> Here's my working (pls check it - I may have made mistakes)
>>> _________________
>>> For brevity, I'll define some briefer variable names:
>>>          interval =      I [s]
>>>          next_drop =     D [s]
>>>          packet-rate =   R [pkt/s]
>>>          count =         n [pkt]
>>>
>>> >From the CoDel control law code:
>>>          D(n) = I / sqrt(n)
>>> And the instantaneous drop probability is:
>>>          p(n) = 1/( R * D(n) )
>>>
>>> Then the slope of the rise in drop probability with time is:
>>>          Delta p / Delta t       = [p(n+1) - p(n)] / D(n)
>>>                                  = [1/D(n+1) - 1/D(n)] / [ R * D(n) ]
>>>                                  = sqrt(n) * [sqrt(n+1) - sqrt(n)] /
>>> [R*I*I]
>>>                                  = [ sqrt(n(n+1)) - n ] / R*I^2
>>>
>>> I couldn't find anything wrong with the derivation. I'm not entirely
>>> sure that I think it makes sense to speak about an "instantaneous drop
>>> probability" for an algorithm that is not probabilistic in nature.
>>> However, interpreting p(n) as "the fraction of packets dropped over the
>>> interval from D(n) to D(n+1)" makes sense, I guess, and for this
>>> analysis that works.
>>>
>>> At count = 1, the numerator starts at sqrt(2)-1 = 0.414.
>>> Amd as n increases, it rapidly tends to 1/2.
>>>
>>> So CoDel's rate of increase of drop probability with time is nearly
>>> constant (it
>>> is always between 0.414 and 0.5) and it rapidly approaches 0.5 after a few
>>> drops, tending towards:
>>>          dp/dt = 1/(2*R*I^2)
>>>
>>> This constant increase clearly has very little to do with the square-root
>>> law of
>>> TCP Reno.
>>>
>>> In the above formula, drop probability increases inversely proportional to
>>> the
>>> packet rate. For instance, with I = 100ms and 1500B packets
>>> at 10Mb/s =>    R = 833 pkt/s =>        dp/dt = 6.0% /s
>>> at 100Mb/s =>   R = 8333 pkt/s =>       dp/dt = 0.6% /s
>>>
>>> I also tried to test this. I configured CoDel (on a Linux 4.0 box) on
>>> 1Mbps, 2Mbps and 10Mbps links with interval settings of 1 second and
>>> 500ms, and a total packet limit of 100k packets. This was to make it
>>> deliberately slower to react (so the change in drop probability is more
>>> visible), and to make sure no packets are dropped from queue overflow.
>>>
>>> I then sent an unresponsive UDP stream over the link at 110% of the link
>>> capacity (as passed to Iperf, so approximately), and collected the
>>> output of `tc -s qdisc` every 0.2 seconds.
>>>
>>> The attached plot is of 'pkts dropped / (pkts sent + pkts dropped)' in a
>>> 2-second sliding window over the duration of the test (the plot is also
>>> available here:
>>> https://kau.toke.dk/ietf/codel-drop-rate/codel-drop-rate.svg ).
>>>
>>> I've included linear trend lines from the initial time to the point of
>>> maximum drop probability, and as is apparent from the plot, got quite a
>>> good fit (r>0.99 for all six data sets). The legend includes the slopes
>>> of the linear fits for each of the data sets, which are not too far from
>>> what your analysis predicts (and I'm guessing the difference can be
>>> attributed to the difference in exact packet rates, but I haven't
>>> checked).
>>>
>>> The Flent data files with the qdisc stats over time (readable by the
>>> newest git version of Flent), as well as the Python script I used to
>>> create the graph are available here:
>>> https://kau.toke.dk/ietf/codel-drop-rate/
>>>
>>> So, in short: It seems that CoDel's "drop rate" does increase linearly
>>> in the presence of a persistent queue, and that the rate of increase
>>> depends on both the interval and the link rate.
>>>
>>> Now, I'll refrain from commenting on whether or not this is "bad", or
>>> indeed if it is contrary to design. It was surprising to me at least, so
>>> I thought I'd share my findings, in the hope that someone would either
>>> find them useful or tell me how they're wrong (or both!). :)
>>>
>>> -Toke
>>>
>>>
>>>
>> Andrew McGregor | SRE | [email-masked] | +61 4 1071 2221
>>
>>
>> --

---

From: Andrew Mcgregor <[email-masked]>
Date: Mon, 26 Oct 2015 09:44:49 +1100

CoDel does have the form of a controller; drop rate (not probability) is a
function of sojourn time (not queue size) and history, encoded in the state
variables.

Now, I don't take it as proven that the particular form of the controller
is the best we could do, but making it a rate and based on sojourn time are
clear wins. Yes, you can use size as a proxy for sojourn time if your link
really has a constant bit rate, but not even ethernet is exactly CBR in
practice (and in some hardware situations, knowing the size is much more
expensive than measuring sojourn; the opposite can also apply).  Yes, you
can use probability as a proxy for rate if you are doing a hardware
implementation or otherwise doing the rate the way CoDel does is hard.

PIE is closely related; the controller is the biggest difference, and it
uses both those proxies because it was aimed at a situation where there was
hardware support for one set of choices.