improving adaptability and fairness in internet congestion control
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Improving Adaptability and Fairness in Internet Congestion
Control
May 30, 2001
Seungwan RyuPhD Student of IE Department
University at Buffalo
2
I. Internet Congestion Control
Internet Congestion Control Mathematical Modeling and Analysis Adaptive AQM and User Response Future Study Plan
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)
4
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
5
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
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)
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
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.
9
cwnd
W W+1
RTT
TCP Congestion Control - 2
Slow-start and Congestion Avoidance
1
2
4
RTT
Slow Start
Congestion Avoidance
Time
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
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
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
13
P
RED
1
maxp
mint
h
maxth K
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
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
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
17
Contents
Internet Congestion Control Mathematical Modeling and
Analysis Adaptive AQM and User Response Future Study Plan
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
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)
20
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
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
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
23
Mathematical Modeling of AQM - 3
s1
S2
SS
AQM Router Destination
Sources
BottleneckLink
1
C
2
S
Min_thK
A simple AQM model
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 ,
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
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
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
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
29
Problems with existing AQMs
Mismatch between macroscopic and microscopic behavior of queue length
Insensitivity to the input traffic load variation
parameter configuration problem
30
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
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
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
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
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
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)
36
Contents
Internet Congestion Control Mathematical Modeling and Analysis Adaptive AQM and User Response Future Study Plan
37
III. Adaptive AQM and User Response
Input traffic load Prediction Adaptive AQM algorithms Adaptive parameter configuration Adaptive User response algorithm
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
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
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
tˆ
3t2
1t2t1t11t2,ˆ,ˆ
1tˆ
1tˆ
1tˆ
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
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
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)
44
Adaptive parameter configuration - 2
(i+2)(i+1)i(i-1) Time
Q
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
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 /
47
Contents
Internet Congestion Control Mathematical Modeling and Analysis Adaptive AQM and User Response Future Study Plan
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
49
Future Study plan: a schedule
Documentation: Mathematical Modeling and Analysis Simulation plan Performance Metrics
50
(*,p*)
p
Mathematical Modeling and Analysis
Since p=f(,q) ,
Then find equilibrium point (*,p*)
pR
pCpqT
)1()1(),(
P=f()=g(p)
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+
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
53
Mathematical Modeling and Analysis - 3
Control Theoretic Consideration
ACK (or NACK)
t(1-p)t
Control Functio
n
Queue dynamic
s
RouterBufferS D
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
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
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