Advanced Computer Networking Active Queue Management.

Advanced Computer Advanced Computer

NetworkingNetworking

Active Queue Management

TCP & AQMTCP & AQM

xi(t)

pl(t)

TCP:

Reno

Vegas

AQM:

DropTail

RED

REM,PI,AVQ

Example congestion measure pl(t)

– Loss (Reno)– Queuing delay (Vegas)


– Loss (Reno)– Queuing delay (Vegas)

22behnam shafagaty behnam shafagaty

Active queue managementActive queue management

• Main idea :: provide congestion information by some indications.

• Issues– How to measure congestion?– How to feed back congestion info?


Active Queue Active Queue ManagementManagement

• Goals:– The primary goal is to provide

congestion avoidance by controlling the average queue size such that the router stays in a region of low delay and high throughput.

– To avoid global synchronization (e.g., in Tahoe TCP).

– To control misbehaving users (this is from a fairness context).


Algorithm 1: Drop TailAlgorithm 1: Drop Tail

55

• FIFO queuing mechanism that drops packets from the tail when the queue overflows.

• Introduces global synchronization when packets are dropped from several connections.

behnam shafagaty behnam shafagaty

66

Early Random Drop Early Random Drop RouterRouter

• If the queue length exceeds a drop level, then the router drops each arriving packet with a fixed drop probability p.

• Reduces global synchronization

• Does not control misbehaving users (UDP)

p

Drop level


RED/ECNRED/ECN Router Router MechanismMechanism

77

1

0

AverageAverage Queue LengthQueue Length

minth maxth

Dropping/Marking

Probability

Queue Size

maxp


RED AlgorithmRED Algorithm

for each packet arrivalcalculate the average queue size avgif minth ≤ avg < maxth

calculate the probability pa

with probability pa:

mark the arriving packetelse if maxth ≤ avg

mark all the arriving packet.

behnam shafagaty behnam shafagaty 88


avgavg - average queue - average queue lengthlength

avg=(1–wq)xavg+wq xq

where q is the newly measured queue length.

This exponential weighted moving average is designed such that short-term increases in queue size from bursty traffic or transient congestion do not significantly increase average queue size.


REDRED drop probability drop probability ( ( ppa a ))

pb = maxp x (avg - minth)/(maxth – minth)

thenpa = pb/ (1 - count x pb)

Where, count is number of consecutive packets queued since last discard while in the critical region.

REDRED parameter settings parameter settings• wq suggest 0.001 <= wq <= 0.0042

authors use wq = 0.002 for simulations• minth, maxth depend on desired average queue size

– bursty traffic increase minth to maintain link utilization.

– maxth depends on the maximum average delay allowed.

– RED is most effective when maxth - minth is larger than typical increase in calculated average queue size in one round-trip time.

– “parameter setting rule”: maxth at least twice minth . However, maxth = 3 times minth is used in some of the experiments shown.


Packet-marking probabilityPacket-marking probability

• The goal is to uniformly spread out the marked packets. This reduces global synchronization.

Method 1: geometric random variableWhen each packet is marked with probability pb,,

the packet inter-marking time, X, is a geometric random variable with E[X] = 1/pb.

• This distribution will both cluster packet drops and have some long intervals between drops!!


packet-marking probabilitypacket-marking probability

Method 2: uniform random variableMark packet with probability

pb/ (1 - count x pb)

where count is the number of unmarked packets that have arrived since last marked packet.


1414

Method 1: geometric p = 0.02Method 2: uniform p = 0.01Result :: marked packets more clustered for

method 1 Uniform is better at eliminating “bursty drops”


Setting Setting maxmaxpp

• “RED performs best when packet-marking probability changes fairly slowly as the average queue size changes.”– This is a stability argument in that the claim is

that RED with small maxp will reduce oscillations in avg and actual marking probability.

• They recommend that maxp never be greater than 0.1

{This is not a robust recommendation.}


Variant: ARED Variant: ARED (Feng, Kandlur, Saha, Shin (Feng, Kandlur, Saha, Shin

1999)1999)

• Motivation: RED extremely sensitive to #sources

• Idea: adapt maxp to load

– If avg. queue < minth, decrease maxp

– If avg. queue > maxth, increase maxp

• No per-flow information needed


Variant: FRED Variant: FRED (Ling & Morris 1997)(Ling & Morris 1997)

• Motivation: marking packets in proportion to flow rate is unfair (e.g., adaptive vs unadaptive flows)

• Idea:

– A flow can buffer up to minq packets without being marked

– A flow that frequently buffers more than maxq packets gets penalized

– All flows with backlogs in between are marked according to RED

– No flow can buffer more than avgcq packets persistently

• Need per-active-flow accounting1818


Variant: SRED Variant: SRED (Ott, Lakshman & Wong (Ott, Lakshman & Wong

1999)1999) • Motivation: wild oscillation of queue in

RED when load changes• Idea:

– Estimate number N of active flows• An arrival packet is compared with a

randomly chosen active flows• N ~ prob(Hit)-1

– cwnd~p-1/2 and Np-1/2 = Q0 implies p = (N/Q0)2

• No per-flow information needed1919


Variant: BLUE Variant: BLUE (Feng, Kandlur, Saha, Shin (Feng, Kandlur, Saha, Shin

1999)1999) Idea: perform queue management based directly on packet loss and link utilization rather than on the instantaneous or average queue lengths.


REM REM (Athuraliya & Low 2000)(Athuraliya & Low 2000)

• Congestion measure: pricepl(t+1) = [pl(t) + g(al bl(t)+ xl

(t) - cl )]+

• Embedding: exponential probability function

• Feedback: dropping or ECN marking

0 2 4 6 8 10 12 14 16 18 200

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Link congestion measure

Lin

k m

arkin

g probability


Match rate

Key featuresKey features

• Clear buffer and match rate

Clear buffer

)] )(ˆ )( ()([ )1( ll

llll ctxtbtptp

)()( 1 1 tptp sl

Sum prices


TCP/AQM ModelsTCP/AQM Models


TCP & AQMTCP & AQM

xi(t)

pl(t)


– Loss (Reno)– Queueing delay (Vegas)


Macroscopic View of TCP Macroscopic View of TCP ControlControl

•TCP/AQM: A feedback control system

TCP Sender 1TCP Sender 1C

xi(t)

TCP:

Reno

Vegas

FAST

AQM:

DropTail / RED

Delay

ECN

TCP Sender 2TCP Sender 2

q(t)

TCP Receiver 1TCP Receiver 1

TCP Receiver 2TCP Receiver 2

Bii tq,txFtx

ctxtqGtq Fi

i ,

τF

τB


Fluid ModelsFluid Models

Assumptions:• TCP algorithms directly control the transmission

rates;• The transmission rates are differentiable (smooth);• Each TCP packet observes the same congestion

price (loss, delay or ECN)

Bii tq,txFtx

ctx,tqGtq Fi

i


OutlineOutline

Protocol (Reno, Vegas, RED, REM/PI…)

Equilibrium

Performance

• Throughput, loss, delay

Fairness

Utility

Dynamics

Local stability

Cost of stabilization

))( ),(( )1(

))( ),(( )1(

txtpGtp

txtpFtx


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Advanced Computer Networking Active Queue Management.

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