Polina, Roland,
This is good info.
So, here is a short summary of our analysis -
For FQ-PIE with aggregate-queue AQM -
1.In the presence of unresponsive flows, FQ-PIE has similar properties
as single-queue AQMs - the responsive flows are squeezed down to use
leftover bandwidth, if any. FQ-AQM with per-queue AQM performs better.
2.In the presence of flows that do not use their fairshare
(temporarily or permanently), FQ-PIE has similar properties as
single-queue AQMs - the flows, that do not use their fairshare,
experience non-zero packet drops. FQ-AQM with per-queue AQM performs
better.
3.In the presence of flows that do not use their fairshare
(temporarily or permanently), the queue size and queuing delay of
flows that use their fairshare can grow above the desired target value.
#2 and #3 are probably not major issues - especially in a network
bottleneck with a large number of diverse flows.
But it is worth pointing out and documenting these properties (somewhere).
Regards,
Anil
*From:*Polina Goltsman [mailto:[email protected]]
*Sent:* Tuesday, July 07, 2015 5:09 AM
*To:* Bless, Roland (TM); Agarwal, Anil; Fred Baker (fred); Toke
Høiland-Jørgensen
*Cc:* [email protected]; Hironori Okano -X (hokano -
AAP3 INC at Cisco); AQM IETF list
*Subject:* Re: [aqm] FQ-PIE kernel module implementation
Hello all,
Here are my thoughts about interaction of AQM and fair-queueing system.
I think I will start with a figure. I have started a tcp flow with
netperf, and 15 seconds later unresponsive UDP flow with iperf with a
send rate a little bit above bottleneck link capacity. Both flows run
together for 50 seconds.
This figure plots the throughput of UDP flow that was reported by
iperf server. (Apparently netperf doesn't produce any output if
throughput is below some value, so I can't plot TCP flow.). The
bottleneck is 100Mb/s and RTT is 100ms. All AQMs were configured with
their default values and noecn flag.
Here is my example in theory. A link with capacity is C is shared
between two flows - a non-application-limited TCP flow and
unresponsive UDP flow with send rate 105%C. Both flows send max-sized
packets, so round robin can be used instead of fair-queueing scheduler.
Per definition of max-min fair share both flows are supposed to get
50% of link capacity.
(1) Taildrop queues:
UDP packets will be dropped when its queue is full, TCP packets will
be dropped when its queue is full. As long as there are packets in TCP
flow queue, TCP should receive its fair share. ( As far as I
understand, this depends on the size of the queue)
(2) AQM with state per queue:
Drop probability of UDP flow will always be non-zero and should
stabilize around approximately 0.5.
Drop probability of TCP flow will be non-zero only when it starts
sending above 50%C. Thus, while TCP recovers from packet drops, it
should not receive another drop.
(3) AQM with state per aggregate:
UDP flow always creates a standing queue, so drop probability of
aggregate is always non-zero. Let's call it /p_aqm/.
The share of TCP packets in the aggregate /p_tcp = TCP send rate /
(TCP send rate + UDP send rate)/ and the probability of dropping a TCP
packet is /p_aqm * p_tcp. /This probability is non-zero unless TCP
doesn't send at all.
In (3) drop probability is at least different. I assume that it is
larger than in (2), which will cause more packet drops for TCP flow,
and as result the flow will reduce its sending rate below its fair share.
Regards,
Polina
On 07/07/2015 10:06 AM, Bless, Roland (TM) wrote:
Hi,
thanks for your analysis. Indeed, Polina came up with
a similar analysis for an unresponsive UDP flow and
a TCP flow. Flow queueing can achieve link share fairness
despite the presence of unresponsive flows, but is ineffective
if the AQM is applied to the aggregate and not to the individual
flow queue. Polina used the FQ-PIE implementation
to verify this behavior (post will follow).
Regards,
Roland
Am 04.07.2015 um 22:12 schrieb Agarwal, Anil:
Roland, Fred,
Here is a simple example to illustrate the differences between FQ-AQM
with AQM per queue vs AQM per aggregate queue.
Let's take 2 flows, each mapped to separate queues in a FQ-AQM system.
Link rate = 100 Mbps
Flow 1 rate = 50 Mbps, source rate does not go over 50 Mbps
Flow 2 rate >= 50 Mbps, adapts based on AQM.
FQ-Codel, AQM per queue:
Flow 1 delay is minimal
Flow 1 packet drops = 0
Flow 2 delay is close to target value
FQ-Codel, AQM for aggregate queue:
Does not work at all
Packets are dequeued alternatively from queue 1 and queue 2
Packets from queue 1 experience very small queuing delay
Hence, CoDel does not enter dropping state, queue 2 is not
controlled :(
FQ-PIE, AQM per queue:
Flow 1 delay is minimal
Flow 1 packet drops = 0
Flow 2 delay is close to target value
FQ-PIE, AQM for aggregate queue:
Flow 1 delay and queue 1 length are close to zero.
Flow 2 delay is close to 2 * target_del :(
qlen2 = target_del * aggregate_depart_rate
Flow 1 experiences almost the same number of drops or ECNs as flow
2 :(
Same drop probability and almost same packet rate for both
flows
(If flow 1 drops its rate because of packet drops or ECNs, the
analysis gets slightly more complicated).
See if this makes sense.
If the analysis is correct, then it illustrates that flow behaviors are
quite different
between AQM per queue and AQM per aggregate queue schemes.
In FQ-PIE for aggregate queue,
- The total number of queued bytes will slosh between
queues depending on the nature and data rates of the flows.
- Flows with data rates within their fair share value will
experience
non-zero packet drops (or ECN marks).
- Flows that experience no queuing delay will increase queuing
delay of other flows.
- In general, the queuing delay for any given flow will not be
close to target_delay and can be
much higher
