Internet Congestion Control with Active Queue Management (AQM)

Internet Congestion Control with Active Queue

Management (AQM)

September 4, 2001

Seungwan Ryu([email protected])

PhD Student of IE DepartmentUniversity at Buffalo

2

Contents

Internet Congestion Control Mathematical Modeling and Analysis Adaptive AQM and User Response Further studies

3

I. Internet Congestion Control

Internet Traffic Engineering What is Congestion ? Congestion Control and Avoidance Implicit vs. Explicit feedback TCP Congestion Control Active Queue management (AQM) Explicit Congestion Notification (ECN)

4

Internet Traffic Engineering

Measurement: for reality check Experiment: for Implementation Issues Analysis:

Bring fundamental understanding of systems May loose important facts because of

simplification Simulation:

Complementary to analysis: Correctness, exploring complicate model

May share similar model to analysis

5

What is congestion ? What is congestion ?

The aggregate demand for bandwidth exceeds the available capacity of a link.

What will be occur ? Performance Degradation

• Multiple packet losses• Low link utilization (low Throughput)• High queueing delay• Congestion collapse

6

What is congestion ? - 2

Congestion Control

Open-loop control Mainly used in circuit switched network (GMPLS)

Closed-loop control Mainly used in packet switched network Use feedback information: global & local

Implicit feedback control End-to-end congestion control Examples:TCP Tahoe, TCP Reno, TCP Vegas, etc.

Explicit feedback control Network-assisted congestion control Examples:IBM SNA, DECbit, ATM ABR, ICMP source quench, RED, ECN

7

Congestion Control and Avoidance

Two approaches of handling Congestion

Congestion Control (Reactive)• Play after the network is overloaded

Congestion Avoidance (Proactive)• Play before the network becomes

overloaded

8

Implicit vs. Explicit feedback

Implicit feedback Congestion Control

Network drops packets when congestion occur

Source infers congestion implicitly• time-out, duplicated ACKs, etc.

Example: end-to-end TCP congestion Control

Simple to implement but inaccurate • implemented only at transport layer (e.g., TCP)

9

Implicit vs. Explicit feedback - 2

Explicit feedback Congestion Control Network component (e.g., router) provides

congestion indication explicitly to sources• use packet marking, or RM cells (in ATM ABR

control) Examples: DECbit, ECN, ATM ABR CC, etc. Provide more accurate information to sources But is more complicate to implement

• Need to change both source and network algorithm• Need cooperation between sources and network

component

10

TCP Congestion Control

Uses end-to-end congestion control uses implicit feedback

• e.g., time-out, triple duplicated ACKs, etc. uses window based flow control

• cwnd = min (pipe size, rwnd)• self-clocking• slow-start and congestion avoidance

Examples:• TCP Tahoe, TCP Reno, TCP Vegas, etc.

11

TCP Congestion Control - 2

Slow-start and Congestion Avoidance

cwnd

TimeRTT

1

2

4

Slow Start

W*

W W+1

RTT

Congestion Avoidance

W*/2

12


TCP Tahoe Use slow start/congestion avoidance Fast retransmit: an enhancement

detect packet (segments) drop by three duplicate ACKs W = W/2, and enter congestion avoidance

TCP Reno (fast recovery) Upon receiving three duplicate ACKs

ssthresh = W/2, and retransmit missing packets W = ssthresh +3

Upon receiving next ACK: W = ssthresh Allow the window size grow fast to keep the pipeline

full

13


TCP SACK (Selected Acknowledgement) TCP (Thaoe) sender can only know about a single lost

per RTT SACK option provides better recovery from multiple

losses The sender can transmit all lost packets But those packets may have already been received

Operation Add SACK option into TCP header The receiver sends back SACK to sender to inform the

reception of the packet Then, the sender can retransmit only the missing packet

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Active Queue Management (AQM) - 1

Performance Degradation in current TCP Congestion Control Multiple packet loss Low link utilization Congestion collapse

The role of the router becomes important Control congestion effectively in networks Allocate bandwidth fairly

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AQM - 2

Problems with current router algorithm Use FIFO based tail-drop (TD) queue management Two drawbacks with TD: lock-out, full-queue

Lock-out: a small number of flows monopolize usage of buffer capacity Full-queue: The buffer is always full (high queueing delay)

Possible solution: AQM Definition: A group of FIFO based queue management

mechanisms to support end-to-end congestion control in the Internet

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AQM - 3 Goals of AQM

Reducing the average queue length: Decreasing end-to-end delay

Reducing packet losses: More efficient resource allocation

Methods: Drop packets before buffer becomes full Use (exponentially weighted) average queue

length as an congestion indicator Examples: RED, BLUE, ARED, SRED, FRED,

….

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AQM - 4 Random Early Detection (RED)

use network algorithm to detect incipient congestion

Design goals:• minimize packet loss and queueing delay• avoid global synchronization• maintain high link utilization• removing bias against bursty source

Achieve goals by• randomized packet drop• queue length averaging

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RED

P

1

maxp

mint

h

maxth K

QWavgWavg QQQQ )1(

Qth

thQththth

thQ

thQ

d

avg

avgavg

p

avg

P

max1

maxminminmax

minmin0

max

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AQM - 5 : BLUE Concept

To avoid drawbacks of RED Parameter tuning problem Actual queue length fluctuation

Decouple congestion control from queue length

Use only loss and idle event as an indicator

Maintains a single drop prob., pm

Drawback Can not avoid some degree of

multiple packet loss and/or low utilization

Algorithm

Upon packet loss if (now - last_update

>freeze_t) Pm = pm + d1 last_update = now

upon link idle if (now - last_update

>freeze_t) Pm = pm - d2 last_update = now

20

AQM - 6 : SRED Concept

stabilize queue occupancy use actual queue length Penalize misbehaving flows

Drawbacks P(i)-1 is not a good estimator

for heterogeneous traffic Parameter tuning problem:

Psred, Pzap, etc. Stabilize queue occupancy

when traffic load is high. (When load is low ?)

Algorithm ith arriving packet is compared with

a randomly selected one from Zombie list

Hit = 1, if they are from same flow = 0, if NOT p(i)=hit frequency=(1-)p(i-

1)+Hit p(i)-1: estimator of # of active flows Packet drop probability

Bq

BqBp

BqBp

psred

)6/1(0

)3/1()6/1()4/1(

)3/1(

max

max

)))(256(

1,1min(*

2iPPP sredzap

21

AQM - 7 : ARED

Adapt aggresiveness of RED according to the traffic load change adapt maxp based on queue behavior

Operation Increase maxp when avgQ crosses above maxth

Decrease maxp when avgQ crosses below minth

freeze maxp after changing to prevent oscillation

22

AQM - 8

Problems with existing AQM Proposals Mismatch between macroscopic and

microscopic behavior of queue length Insensitivity to the change of input traffic

load Configuration (parameter setting) problem

Reasons: Queue length averaging use inappropriate congestion indicator Use inappropriate control function

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Explicit Congestion Notification (ECN)

Current congestion indication Use packet drop to indicate congestion source infer congestion implicitly

ECN to give less packet drop and better performance use packet marking rather than drop need cooperation between sources and network need two bits in IP header: ECT-bit, CE-bit

24

ECN - 2

1

TCP Header

ECT CE

1 0IP Header

CWR

0

ECT CE

CWR

2

1 1

0

3

ACK TCPHeader

ECN-Echo

1

4

TCP Header

CWR

1

Source Router Destination

25

Contents

Internet Congestion Control Mathematical Modeling and

Analysis Adaptive AQM and User Response Further Studies

26

II. Mathematical Modeling and Analysis An Overview

Mathematical Modeling of AQM Window based packet switching and the Internet Mathematical modeling and analysis of AQM

Problems with existing AQMs Problems with existing AQMs Adaptive congestion indicator and control

function

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Overview - 1 Goal of mathematical modeling

See steady state system dynamics Capture main factors influence to performance Provide recommendations for design and operation

Two approaches for TCP Congestion Control Modeling steady state TCP behaviors

• the square root law*, PFTK [Padhye et al., 1998]• assume TD queue management at the router

Mathematical modeling and analysis of AQM (RED)

*: , T: Throughput, p: constant drop rate

pRTT

cT

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Overview - 2

AQM modeling and analysis Analytic modeling and analysis Control Theoretic Analysis Window based modeling and Analysis

Assumptions Poisson assumption for input traffic Fixed number of persistent TCP traffics Steady state window size saturation

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Mathematical Modeling of AQM - 1

Window based packet switching Model (Yang 99) Determine the steady state window size, Ws, of

each flow sS If link j is not congested

If link j is congested

jCs sj

jjSsQn jsj ),(0,0

jCs sj

jjSsQn jsj ),(0,0

30

Mathematical Modeling of AQM - 2 Window equation for an individual flow

Since

Limitation of this model Assume infinite buffer size

• No buffer overflow• No packet drop• No queue management algorithm at routers

)()(

jSsRC

QnRW sj

j

jssJj sjsss

)1(0 jj

s

j

sj QCQ

n

31


s1

S2

SS

AQM Router Destination

Sources

BottleneckLink

1

C

2

S

Min_thK

A simple AQM model

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Extend Yang’s Model to AQM model Finite buffer capacity K The router use AQM to control congestion When congested

• Yang’s Model:

• Our Model: )1(, dsss s pC

sss s C ,

33


Case 1: Tail drop

Packet drop probability Pd:

.w.o0

KQandCif1pd

34


Case 2: AQM Let Then since

Packet drop prob. Pd:

s sth nQQ min

))(1(C

QRpW d

..0

min,)(

1

wO

QCif

CQR

W

pth

d

35


Congestion Indicator Input traffic load should be the congestion

Indicator Current AQMs

• Use queue length Q as an alternative• Assume that the input traffic load is fixed in

equilibrium Reason

• can not measure(or estimate) exactly for on line implementation of packet drop function

36


Packet drop function

Reason• The traffic load fluctuate, NOT stay in

equilibrium• queue length is a function of input traffic

Alternatively:

)(fpd

),( Qfpd

37

Problems with existing AQMs

Mismatch between macroscopic and microscopic behavior of queue length

Insensitivity to the input traffic load variation

parameter configuration problem

38

Problems with existing AQMs - 2

Mismatch problemInternet Traffic Generation

0

5

10

15

20

25

30

35

40

1 4 7 10 13 16 19 22 25 28 31

time

Win

do

w s

ize

39

Problems with existing AQMs - 3 Mismatch between macroscopic and

microscopic behavior of queue length

0

5

10

15

20

25

1 6 11 16 21 26 31

Time

Qu

eue

Len

gth

Rho Actual Wq=0.02 Wq=0.1

2.0

1.5

1.0

0.5

0

40

Problems with existing AQMs - 4 Insensitivity to the input traffic load

variation

Schemes: I:RED, II:GRED, III:

0.00

0.20

0.40

0.60

0.80

1.00

Traffic Intensities (loads)

Pa

ck

et

dro

p r

ate

0.3

0.5

0.7

0.9

1.1

1.3

: u=0.7 : u=0.45 : u=0.25 : RED : GRED : Scheme III

),( Qfpd

41

Problems with existing AQMs - 5

Parameter configuration problem Has been a main design issue since 1993 Many modified AQMs has been proposed

• Verified with simple simulation or simple experiment• good for particular traffic conditions• Real traffic is totally different.

Need adaptive congestion indicator and control function

• Adaptive to input traffic load variation• Avoid congestion NOT based on current state (i,e,. Q)

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Contents

Internet Congestion Control Mathematical Modeling and Analysis Adaptive AQM and User Response Further Studies

43

III. Adaptive AQM and User Response

Input traffic load Prediction Adaptive AQM algorithms Adaptive parameter configuration Adaptive User response algorithm

44

Input traffic load Prediction

Consider time-slotted model Time is divided into unit time slots, t, t=0,1,… calculate parameters at the end of each slot estimate Qt+1 to detect congestion proactively

• Predict from measured input traffic t-1, t of past two time slots

• Then, predict of next time slot t

ttt QCQ )( 11

1tˆ

1tQ̂

45

Adaptive AQM algorithms

Algorithm I: E-RED and E-GRED Enhanced-RED

E-GRED: similar to E-RED

1tth

th1tththth

th1tp

th1t

Q̂max1

maxQ̂minminmax

minQ̂max

minQ̂,0

p

46

Adaptive AQM algorithms - 2 Algorithm II:

Use both predicted traffic intensity and current buffer utilization t=Qt/K

represents imminent traffic changes in near future t represents current status of traffic

Possible algorithms:

1tˆ

tˆ

3t21t2t1t1

1t2,ˆ,ˆ

1tˆ

47

Adaptive AQM algorithms - 3 Example:

maintain Qindex to impose appropriate drop rate adaptively to traffic load change

Then,

• If t is low and is high: more penalty to incoming packets

• If t is high and is low: more penalty on existing packets

• Only High penalty for both packets when t and are high

1tˆ

1tˆ

1tˆ

1ˆ

,,*

),1(*

tt

tindex

indexd

indexdd

QQ

QQwhere

packetsexistingQp

packetsarrivingQpP

48

Adaptive AQM algorithms - 4

Algorithm III: E-BLUE

BLUE Algorithm• uses packet drops and link idle for adjusting packet

drop probability• Can not avoid some degree of performance

degradation

Enhancement• Use Virtual lower/upper bound (VL, VU)• Combine predicted queue length with BLUE

1tQ̂

49

Adaptive parameter configuration

Adaptive queue length sampling interval t Previous recommendations

• In [Firoiu et al.], minimum RTT was recommended• In [Hollot et al.], static and link speed independent

value was recommended• However, above recommendations were obtained from

assumptions of persistent and fixed N TCP traffics Our recommendation

• The amount of incoming traffic fluctuate with time• Adjust t according to the varying traffic situation (i.e., adjust t according to the amount of input traffic)

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Adaptive parameter configuration - 2

(i+2)(i+1)i(i-1) Time

Q

51

Adaptive parameter configuration - 3

Adaptive filtering weight wq

In RED, wq was recommended with 0.002 for long-term (macroscopic) performance goal

Fixed small value of wq shows problems• Parameter setting problem• Insensitivity of control function to the change of traffic• Fairness problem: impose penalty to innocent packets

Need to have adaptive wq to the change of traffic load One possible method:

• Set wq as a function of current queue utilization,

e.g., wq = Qt/C , 0 < < 1

52

Adaptive User response algorithm

AQM need work with intelligent source response for better performance

Enhanced-ECN If receive ECN feedback in (t-1)

• If No ECN feedback in t If received ACK > 0 , W= W+M/W + M Else , W= W+M/W

• Else, Continue usual response to ECN feedback

Else, Continue TCP Congestion Avoidance

53

Contents

Internet Congestion Control Mathematical Modeling and Analysis Adaptive AQM and User Response Further Studies

54

IV. Further Studies

Mathematical Modeling and Analysis Stability and Control Dynamics Alternative Modeling Control Theoretic Consideration

Simulation studies Traffics Performance Metrics

Other approaches of congestion control More about AQM

55

(*,p*)

p

Mathematical Modeling and Analysis

Since p=f(,q) ,

Then find equilibrium point (*,p*)

pR

)p1(C)p1()q,(T

P=f()=g(p)

56

Mathematical Modeling and Analysis - 2

Alternative Modeling: State dependent service M/M/1/K queueing

model

L=minth, K’=K-minth

(C+pK’-1)CC (C+p1)C

10

LL-1

L-1

KK-1

C+

57


Service rates

Steady state probabilities

0i,)C

()pC(

)C

(

Kimin)C

()pC(

mini)C

(

1K

1mini

minmini1j

i

min

1i

i

th,0minmini

1ji

th,0i

i

th

ththth

thth

i

ithi

thi

QK,C

KQmin,pC

minQ,C

S

58


Control Theoretic Consideration

ACK (or NACK)

t(1-p)t

Control Functio

n

Queue dynamic

s

RouterBufferS D

59

Simulation study

Goal of simulation study See dynamics and performance of our AQM Compare results with other AQM such as RED

Use realistic traffic previous studies has been done with simple

and unreal traffic (fixed number of persistent TCPs)

Generate realistic Internet traffic• Long-lived (FTP) and short-lived (web-like) TCP traffic• UDP traffic: CBR and/or ON/OFF

60

Performance Metrics

TCP traffics Network-centric: for aggregate traffic

• Throughput (or goodput)• Packet dropping (marking) probability• Link utilization (or queueing delay)

User-centric: for Individual traffic• goodput (or throughput)• mean response time (RTT)

UDP traffic• individual packet drop probability and its

distribution

61

Other approaches of CC - 1: Pricing

Smart-market [Mackie-Mason 1995] A price is set for each packet depends on the level of

demand for mandwidth Admit packets with bid prices that exceed the cut-off

value The cut-off is determined by the marginal cost

Paris metro pricing (PMP) [Odlyzko] To provide differentiated services The network is partitioned into several logical separate

channels with different prices With less traffic in channel with high price, better QoS

would be provided.

62

Other approaches - 2: Optimization

Concept Network resource

allocation problem: User problems Network problems

User problem sends bandwidth request with pricie

Network problem allocate bandwidth to each users by solving NLP

User problem Users can be distinguished

by a utility function A user wants to maximize its

benefit (utility - cost)

Network problem maximize aggregate utilities

subject to the link capacity constraints

Then, it can be formulated to a Non-linear programming (NLP) problem

63

Other approaches - 3: Fairness Two fairness issues

Fair bandwidth sharing: network-centric Fair packet drop (mark): user-centric

Fair bandwidth sharing Max-min fair [Bertsekas, 1992]:

No rate can be increased without simultaneous decreasing other rate which is already small

provides equal treatment to all flows Proportional fair [Kelly 1998]

A feasible set of rates are non-negative and the aggregate rate is not greater than link capacity and the aggregate of proportional change is zero or negative

provides different treatment of each flow according to their rates

64

More about AQM

Responsive (TCP) vs. unresponsive flows (UDP) RED fail to regulate unresponsive flows

UDP do not adjust sending rate upon receiving congestion signal UDP flows consumes more bandwidth than fair share

FRED [Lin & Morris, 1997] Tracks the # of packets in the queue from each flow

maintain logical queues for each active flows in a FIFO queue Fair share for a flow is calculated dynamically unresponsive flows are identified and penalized

Drop packets proportional to bandwidth usage See TCP-friendly website

(http://www.psc.edu/networking/tcp_friendly.html)

65

More about AQM - 2

Providing QoS and DiffServ with AQM Try to support a multitude of transport protocol

(TCP, UDP, etc.) Classify several types of services rather than

one best-effort service. Then, apply different AQM control to each

services classes. Examples:

RIO (RED In and Out) [Clark98] CBT (Class based Thresholds) [Floyd1995]

66

More about AQM - 3

RIO (RED in and out) [Clark 1998] Separate flows into two classes: IN and OUT service

profile router maintains two different statistics for each service

profiles. Different parameters and average queue lengths Avgs: for IN packet: avgIN, for OUT profile: avgTOTAL

When congested, apply different control to each classes

Pmax_IN

1p

avg

Pmax_OUT

Minth_OUT Maxth_OUT

= Minth_IN

Maxth_IN

67

More about AQM - 4 CBT [Floyd 1995]

packets are classified into several classes

maintain a single queue but allocate fraction of capacity to each class

Apply AQM (RED) based control to each class

Once a class occupies its capacity, discard all arriving packets

Drawbacks Fairness problem in case of

changing traffic mix static threshold setting

Total utilization can be fluctuated

Dynamic-CBT [Chung2000] Track the number of active flows

of each class dynamically adjust threshold

values of each class

68

More about AQM - 5

Other Issues AQM vs. Tail Drop(TD) Congestion Indicator:

Average queue length vs. Instantaneous queue length Parameter tuning problem:

wq, maxp, static or dynamic sampling

Alternative ways: virtual queue approach EX: [Gibbens 1998], [Kuniyur2000]

Performance with/without ECN mechanism Control objective” Router-centric vs. user-centric

69

References S. Floyd et al. “Random early detection gateways for

congestion avoidance control.” IEEE/ACM TON, 1993. RED web page, http://www.aciri.org/floyd/red.html RED for dummies, http://www.magma.ca/~terrim/RedLit.

htm S. Ryu et al. “Advances in Internet congestion control.”

submitted to IEEE comm. Survey & Tutorial, 2001 B. Braden et al. “Recommendations on queue

management and congestion avoidance in the Internet.” IETF RFC2309, 1998.

K. Ramakrishinan et al. “A proposal to add explicit congestion notification (ECN) to IP.” IETF RFC2481, 1999.

Documents

Internet Congestion Control with Active Queue Management (AQM)