Active Queue Management

Active Queue Management

Rong Pan Cisco System

Outline

• Queue Management

– Drop as a way to feedback to TCP sources – Part of a closed-loop

• Traditional Queue Management – Drop Tail

– Problems

• Active Queue Management – RED

– CHOKe – AFD

Queue Management: Drops/Marks

-

• End hosts send TCP traffic -> Queue size

• Network elements, switches/routers, generate drops/marks based on their queue sizes

• Drops/Marks: regulation messages to end hosts • TCP sources respond to drops/marks by cutting

TCP+Queue Management

- A closed-loop control system

Queue Management _

N

0.5

Drop Tail

-

problems

• Lock out

• Full queue

• Bias against bursty traffic

Max Queue Length

Tail Drop Queue Management

Tail Drop Queue Management

Full-Queue

• Only drop packets when queue is full

Max Queue Length

Max Queue Length

Tail Drop Queue Management

• Drop from front on full queue

• Drop at random on full queue

both solve the lock-out problem both have the full-queues problem

Alternative Queue

• Solve tail-drop problems

– no global synchronization – no bias against bursty flow

• Provide better QoS at a router

– lower packet dropping

Active Queue Management

Random Early Detection

(RED)

yes Drop the new packet

end Admit packet with

a probability p AvgQsize > Max_th? yes Arriving packet no Admit the new packet end AvgQsize > Min_th? no

RED Dropping Curve

min_th max_th 0

Average Queue Size

D ro p P ro b a b ili ty 1 max_p

Effectiveness of RED

-

Lock-Out & Global Synchronization

• Packets are randomly dropped

• Each flow has the same probability of being

discarded

• Drop packets probabilistically in anticipation

of congestion

– not when queue is full

• Use q

to decide packet dropping

probability: allow instantaneous bursts

Effectiveness of RED

What QoS does RED

Provide?

• Lower buffer delay: good interactive service

– q_avg is controlled to be small

• Given responsive flows: packet dropping is

reduced

– early congestion indication allows traffic to throttle

back before congestion

Bad News -

unresponsive end hosts

Scheduling & Queue

Management

• What routers want to do?

– isolate unresponsive flows (e.g. UDP) – provide Quality of Service to all users

• Two ways to do it

– scheduling algorithms:

e.g. FQ, CSFQ, SFQ

– queue management algorithms:

FQ vs. RED

• Hard/Expensive to implement

• Isolation from non-adaptive flows

FQ

• No isolation from non-adaptive flows

Active Queue Manament With

Enhancement to Fairness

• Provide isolation from unresponsive flows

• Be as simple as RED

yes Drop the new packet

end Admit packet with

a probability p end AvgQsize > Max_th? yes

RED

the new packet id ?

yes Drop the new packet Admit packet with

a probability p

no AvgQsize > Maxth? no

Random Sampling from

Queue

•

A randomly chosen packet more likely

from the unresponsive flow

Comparison of Flow ID

•

Compare the flow id with the incoming packet

– more acurate

– Reduce the chance of dropping packets from a

Dropping Mechanism

• Drop packets (both incoming and matching

samples )

– More arrival -> More Drop

1Mbps 10Mbps S(2) S(m) S(m+n) m TCP Sources S(m+1) n UDP Sources D(2) D(m) D(m+n) m TCP Sinks D(m+1) n UDP Sinks D(1) 10Mbps

Simulation Setup

Network Setup Parameters

32 TCP flows, 1 UDP flow

All TCP’s maximum window size = 300

All links have a propagation delay of 1ms

FIFO buffer size = 300 packets

All packets sizes = 1 KByte

32 TCP, 1 UDP (one sample)

) UDP Throughput (RED)

UDP Throughput (CHOKe)

Avg. TCP Throughput (CHOKe)

23.0%

74.1%

32 TCP, 5 UDP (5 samples)

CHOKe(one sample):Total UDP Throughput CHOKe(one sample):Total TCP Throughput CHOKe with 5 samples: Total UDP Throughput CHOKe with 5 samples: Total TCP Throughput

How Many Samples to

Take?

min

ax

Different samples for different Qlen

– # samples

↓

# samples

↑

6.6% 38.3% 61.1% 89.7% 99.3% 0 300 600 900 1200 100 1000 10000 T h ro u g h p u t (K b p s

) Total UDP Throughput

Total TCP Throughput

32 TCP, 5 UDP

(self-adjusting)

Analytical Model

discards from the queue

permeable tube with leakage

Fluid Analysis

N: the total number of packets in the buffer

L

(t): the survial rate for flow i packets

L

(t)

δ

t - L

(t +

δ

t)

δ

t =

λ

δ

t L

(t)

δ

t /N

- dL

(t)/dt =

λ

L

(t) N

L

(0) =

λ

(1-p

)

L

(D) =

λ

(1-2p

)

Model vs Simulation

-

multiple TCPs and one UDP

0 50 100 150 200 250 300 350 0.1 1 10

T

h

ro

u

g

h

p

u

t

Fluid Model

-

Multiple samples

L

(t)

δ

t - L

(t +

δ

t)

δ

t =

M

λ

δ

t L

(t)

δ

t /N

- dL

(t)/dt =

M

λ

L

(t) N

L

(0) =

λ

(1-p

)

L

(D) =

λ

(1-p

)

_-

_M

λ

p

Two Samples

-

multiple TCPs and one UDP

0 20 40 60 80 100 120 140 U D P T h ro u g h p u t( K b p s ) NS Simulation Fluid Model

Two Samples

-

multiple TCPs and two UDP

0 40 80 120 160 200 U D P T h ro u g h p u t( K b p s ) NS Simulation Fluid Model

What If We Use a

Small Amount of

AFD: Goal

• Approximate equal bandwidth allocation

queues

• Keep the state requirement small

• Be simple to implement

AFD Algorithm: Details

(Basic Case: Equal Share)

Di = Drop Probability for Class i

Qref 1-D_i D_i Qlen Arriving Packets If M_i ≤≤≤≤ Mfair : No Drop (D_i = 0) M_i = Arrival estimate for Class i

(Bytes over interval T_s)

Mfair = Mfair - a (Qlen - Qref) + b (Qlen_old - Qref)

Fair Share Class i

AFD Algorithm: Details

(General Case)

Di = Drop Probability for Class i

Qref 1-D_i

D_i

Qlen Arriving Packets

If M_i ≤≤≤≤ F(Mfair,Min_i,Max_i,W_i, …): No Drop (D_i = 0) M_i = Arrival estimate

for Class i

(Bytes over interval T_s)

Mfair = Mfair - a (Qlen - Qref) + b (Qlen_old - Qref)

Fair Share Class i

Not Per-Flow State

AFD Solution: Details

• Based on 3 simple mechanisms

– estimate per “class” arrival rate

• counting per “class” bytes over fixed intervals ( T_s )

• potential averaging over multiple intervals

– estimate deserved departure rate (so as to achieve

the proper bandwidth allocation for the class)

• Observation and averaging of queue length as

measure of congestion

• Functional definition of “fair share” based on

fairness criterion

– perform probabilistic dropping (pre-enqueue) to drive

Mixed Traffic

with Different Levels of Unresponsiveness

0 100 200 300 400 500 Th ro ug hp ut (k bp s)

Drop Probabilities

(note differential dropping)

0 0.05 0.1 0.15 0.2 0.25 0.3

RED CHOKe AFD

D ro p Pr ob ab ili ty

Different Number of TCP

Flows in Each Class

C la s s 1 5 TCP Flows 0 50 100 150 200 time C la s s 2 10 TCP Flows 0 50 100 150 200 time C la s s 3 15 TCP Flows C la s s 4 20 TCP Flows

Different Class Throughput

Comparison

AFD Implementation Issues

• Monitor Arrival Rate

• Determine Drop Probability

• Maximize Link Utilization

Arrival Monitoring

• Keep a counter for each class

– Count the data arrivals (in bytes) of each

class in 10ms interval: arv_i

• Arrival rate of each class is updated every

10ms

– m_i = m_i(1-1/2c_)+arv i

Implementing the Drop

Function

• If M_i ≤ Mfair then D_i = 0

• Otherwise, rewrite the drop function as

Implementing the Drop

Function

FQ

RED

AFD - Summary

CHOKe

AFD

Ideal

Summary

• Traditional Queue Management

synchronization, bias against bursty traffic

• Active Queue Management

– RED: can’t handle unresponsive flows – CHOKe: penalize unresponsive flows

– AFD: provides approximate fairness with