improving adaptability and fairness in internet congestion control

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Improving Adaptability and Fairness in Internet Congestion Control May 30, 2001 Seungwan Ryu PhD Student of IE Department University at Buffalo

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Improving Adaptability and Fairness in Internet Congestion Control. May 30, 2001 Seungwan Ryu PhD Student of IE Department University at Buffalo. I. Internet Congestion Control. Internet Congestion Control Mathematical Modeling and Analysis Adaptive AQM and User Response Future Study Plan. - PowerPoint PPT Presentation

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Page 1: Improving Adaptability and Fairness in Internet Congestion Control

Improving Adaptability and Fairness in Internet Congestion

Control

May 30, 2001

Seungwan RyuPhD Student of IE Department

University at Buffalo

Page 2: Improving Adaptability and Fairness in Internet Congestion Control

2

I. Internet Congestion Control

Internet Congestion Control Mathematical Modeling and Analysis Adaptive AQM and User Response Future Study Plan

Page 3: Improving Adaptability and Fairness in Internet Congestion Control

3

I. Internet Congestion Control

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

Page 4: Improving Adaptability and Fairness in Internet Congestion Control

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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 loss• Low link utilization (low Throughput)• High queueing delay• Congestion collapse

Page 5: Improving Adaptability and Fairness in Internet Congestion Control

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Congestion Control and Avoidance

Two approaches for handling Congestion

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

Congestion Avoidance (Proactive)• Play before the network becomes

overloaded

Page 6: Improving Adaptability and Fairness in Internet Congestion Control

6

Implicit vs. Explicit feedback

Implicit feedback Congestion Control

Network drops packets when congestion occur

Source infer 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)

Page 7: Improving Adaptability and Fairness in Internet Congestion Control

7

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

Page 8: Improving Adaptability and Fairness in Internet Congestion Control

8

TCP Congestion Control

Use end-to-end congestion control use implicit feedback

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

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

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

Page 9: Improving Adaptability and Fairness in Internet Congestion Control

9

cwnd

W W+1

RTT

TCP Congestion Control - 2

Slow-start and Congestion Avoidance

1

2

4

RTT

Slow Start

Congestion Avoidance

Time

Page 10: Improving Adaptability and Fairness in Internet Congestion Control

10

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 (i.e., network) Control congestion effectively with a network Allocate bandwidth fairly

Page 11: Improving Adaptability and Fairness in Internet Congestion Control

11

AQM - 2

Problems with current router algorithm Use FIFO based tail-drop (TD) queue

management Two drawbacks with TD: lock-out, full-queue

Possible solution: AQM Drop packets before buffer becomes full Examples: RED, BLUE, ARED, SRED, FRED,…. Use (exponentially weighted) average queue

length as an congestion indicator

Page 12: Improving Adaptability and Fairness in Internet Congestion Control

12

AQM - 3 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

Page 13: Improving Adaptability and Fairness in Internet Congestion Control

13

P

RED

1

maxp

mint

h

maxth K

Page 14: Improving Adaptability and Fairness in Internet Congestion Control

14

Active Queue Management (AQM) - 4

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

Page 15: Improving Adaptability and Fairness in Internet Congestion Control

15

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

Page 16: Improving Adaptability and Fairness in Internet Congestion Control

16

ECT CE

CWR

ECN - 2

4

3

2

1

TCP Header

ECT CE

1 0IP Header

CWR

0

1 1

0

ACK TCPHeader

ECN-Echo

1

TCP Header

CWR

1

Source Router Destination

Page 17: Improving Adaptability and Fairness in Internet Congestion Control

17

Contents

Internet Congestion Control Mathematical Modeling and

Analysis Adaptive AQM and User Response Future Study Plan

Page 18: Improving Adaptability and Fairness in Internet Congestion Control

18

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

Page 19: Improving Adaptability and Fairness in Internet Congestion Control

19

Overview Goal of mathematical modeling

see system dynamics (in steady state) capture main factors influence to performance provide design and/or operational

recommendations Two approaches

Modeling steady state TCP behaviors• the square root law, PFTK• assume TD queue management at the router

Mathematical modeling and analysis of AQM (RED)

Page 20: Improving Adaptability and Fairness in Internet Congestion Control

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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

Page 21: Improving Adaptability and Fairness in Internet Congestion Control

21

Mathematical Modeling of AQM Window based packet switching Model

(Yang 99)

If link j is not congested

If link j is congested

jCs sj

jjSsQn jsj ),(0,0

jCs sj

jjSsQn jsj ),(0,0

Page 22: Improving Adaptability and Fairness in Internet Congestion Control

22

Mathematical Modeling of AQM - 2 Window size of an individual connection

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

Page 23: Improving Adaptability and Fairness in Internet Congestion Control

23

Mathematical Modeling of AQM - 3

s1

S2

SS

AQM Router Destination

Sources

BottleneckLink

1

C

2

S

Min_thK

A simple AQM model

Page 24: Improving Adaptability and Fairness in Internet Congestion Control

24

Mathematical Modeling of AQM - 4

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

• Our Model:

• Yang’s Model:

)1(, dsss s pC

sss s C ,

Page 25: Improving Adaptability and Fairness in Internet Congestion Control

25

Mathematical Modeling of AQM - 5

Case 1: Tail drop We obtain two relationship

Finally, packet drop probability Pd:

)2(,0

s sjj

s

j

sj CQCQ

n

C

QR

C

QRW s

ss s

)(

Cif

Cif

CQR

W

pd

0

)(1

Page 26: Improving Adaptability and Fairness in Internet Congestion Control

26

Mathematical Modeling of AQM - 6

Case 2: AQM Let Then

Packet drop prob. Pd:

s sth nQQ min

))(1(C

QRpW d

..0

min,)(

1

wO

QCif

CQR

W

pth

d

Page 27: Improving Adaptability and Fairness in Internet Congestion Control

27

Mathematical Modeling of AQM - 7

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

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28

Mathematical Modeling of AQM - 8

Packet drop function

Reason• The traffic load fluctuate, NOT stay in

equilibrium• queue length is a function of input traffic

Alternatively:

)(fpd

),( Qfpd

Page 29: Improving Adaptability and Fairness in Internet Congestion Control

29

Problems with existing AQMs

Mismatch between macroscopic and microscopic behavior of queue length

Insensitivity to the input traffic load variation

parameter configuration problem

Page 30: Improving Adaptability and Fairness in Internet Congestion Control

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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

Page 31: Improving Adaptability and Fairness in Internet Congestion Control

31

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

Page 32: Improving Adaptability and Fairness in Internet Congestion Control

32

Problems with existing AQMs - 4

Insensitivity to the input traffic load variation

With light traffic (i.e., )5.0,3.0

Rho=0.3

0

0.2

0.4

0.6

0.8

1

1 2 3 4 5

Schemes

Rho=0.5

0

0.2

0.4

0.6

0.8

1

1 2 3 4 5

Schemes

Page 33: Improving Adaptability and Fairness in Internet Congestion Control

33

Problems with existing AQMs - 5

Insensitivity to the input traffic load variation

With medium traffic (i.e., )9.0,7.0

Rho=0.7

0

0.2

0.4

0.6

0.8

1

1 2 3 4 5

Schemes

Rho=0.9

0

0.2

0.4

0.6

0.8

1

1 2 3 4 5

Schemes

Page 34: Improving Adaptability and Fairness in Internet Congestion Control

34

Problems with existing AQMs - 6

Insensitivity to the input traffic load variation

With heavy traffic (i.e., )4.1,1.1

Rho=1.1

0

0.2

0.4

0.6

0.8

1

1 2 3 4 5

Schemes

Rho=1.1

0

0.2

0.4

0.6

0.8

1

1 2 3 4 5

Schemes

Page 35: Improving Adaptability and Fairness in Internet Congestion Control

35

Problems with existing AQMs - 7

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)

Page 36: Improving Adaptability and Fairness in Internet Congestion Control

36

Contents

Internet Congestion Control Mathematical Modeling and Analysis Adaptive AQM and User Response Future Study Plan

Page 37: Improving Adaptability and Fairness in Internet Congestion Control

37

III. Adaptive AQM and User Response

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

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38

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

Page 39: Improving Adaptability and Fairness in Internet Congestion Control

39

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

Page 40: Improving Adaptability and Fairness in Internet Congestion Control

40

Adaptive AQM algorithms - 2 Algorithm II:

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

Possible algorithms:

Example: • 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

3t2

1t2t1t11t2,ˆ,ˆ

1tˆ

1tˆ

1tˆ

Page 41: Improving Adaptability and Fairness in Internet Congestion Control

41

Adaptive AQM algorithms - 3 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• Impose penalty according to the traffic situation ( ,

)

1tQ̂

tQ 1tQ̂

Page 42: Improving Adaptability and Fairness in Internet Congestion Control

42

Adaptive AQM algorithms - 4 E-BLUE

If , then pd = pd- Else if VL < <VU,

• Else ( >VU)

• pd=pd+

0)0,Q̂max( 1t

1tQ̂

existingfor)Q̂Q(p

arrivingfor)QQ̂(pp

1ttd

t1tdd

1tQ̂

existingfor1),Q̂Q(pmin

arrivingfor1p

1ttdd

Page 43: Improving Adaptability and Fairness in Internet Congestion Control

43

Adaptive parameter configuration Adaptive queue length sampling interval t

Previous recommendations• In [22], minimum RTT was recommended• In [65], static and link speed independent value

was recommended• However, models of [22, 65] were assumed to have

persistent 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)

Page 44: Improving Adaptability and Fairness in Internet Congestion Control

44

Adaptive parameter configuration - 2

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

Q

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45

Adaptive parameter configuration - 3

Adaptive filtering weight wq

In RED, wq was recommended with 0.02 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

Page 46: Improving Adaptability and Fairness in Internet Congestion Control

46

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

Else • Else, Continue usual response to ECN feedback

Else, Continue TCP Congestion Avoidance

MWMWW /

WMWW /

Page 47: Improving Adaptability and Fairness in Internet Congestion Control

47

Contents

Internet Congestion Control Mathematical Modeling and Analysis Adaptive AQM and User Response Future Study Plan

Page 48: Improving Adaptability and Fairness in Internet Congestion Control

48

IV. Future Study Plan

Future Study plan: a schedule Mathematical Modeling and Analysis

Stability and Control Dynamics Alternative Modeling Control Theoretic Consideration

Simulation plan Traffics Performance Metrics

Page 49: Improving Adaptability and Fairness in Internet Congestion Control

49

Future Study plan: a schedule

Documentation: Mathematical Modeling and Analysis Simulation plan Performance Metrics

Page 50: Improving Adaptability and Fairness in Internet Congestion Control

50

(*,p*)

p

Mathematical Modeling and Analysis

Since p=f(,q) ,

Then find equilibrium point (*,p*)

pR

pCpqT

)1()1(),(

P=f()=g(p)

Page 51: Improving Adaptability and Fairness in Internet Congestion Control

51

Mathematical Modeling and Analysis - 2

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

model

L=minth, K’=K-minth

(C+pK’-1)CC (C+p1)C

10

LL-1

L-1

KK-1

C+

Page 52: Improving Adaptability and Fairness in Internet Congestion Control

52

Mathematical Modeling and Analysis - 3

Service rates

Steady state probabilities

i

ithi

thi

QK,C

KQmin,pC

minQ,C

S

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

Page 53: Improving Adaptability and Fairness in Internet Congestion Control

53

Mathematical Modeling and Analysis - 3

Control Theoretic Consideration

ACK (or NACK)

t(1-p)t

Control Functio

n

Queue dynamic

s

RouterBufferS D

Page 54: Improving Adaptability and Fairness in Internet Congestion Control

54

Simulation plan

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

Page 55: Improving Adaptability and Fairness in Internet Congestion Control

55

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