Diffusion Early Marking Department of Electrical and Computer Engineering University of Delaware May...
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Diffusion Early Marking Department of Electrical and Computer Engineering University of Delaware May / 2004 Rafael Nunez [email protected] Gonzalo Arce [email protected]
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Transcript of Diffusion Early Marking Department of Electrical and Computer Engineering University of Delaware May...
Diffusion Early Marking
Department of Electrical and Computer EngineeringUniversity of Delaware
May / 2004
Rafael Nunez
Gonzalo Arce
2
Diffusion Early Marking
Introduction Diffusion Early Marking Model Optimizations. Parameters Estimation Performance Conclusions and Future Work
3
The Internet Today
4
Congestion
Desirable control: distributed, simple, stable and fair.
5
Problems with Tail Dropping
Penalizes bursty traffic
Discriminates against large propagation delay connections.
Global synchronization.0 2 4 6 8 10 12 14 16 18 20
0
10
20
30
40
50
60
70
80
90
100Instantaneous Queue Size - Drop Tail
Time (seconds)
Que
ue (
Pac
kets
)
6
Active Queue Management (AQM)
Random Early Detection (Floyd and Jacobson, 1993)
Router becomes active in congestion control.
RED has been deployed in some Cisco routers.
7
Random Early Detection (RED)
Random packet drops in queue. Drop probability based on average queue:
q n6@
= 1- wq
_ i$q n - 1
6 @+wq$q n
6@
Four parameters: qmin qmax Pmax wq
(overparameterized)
8
Queue Behavior in RED
0 2 4 6 8 10 12 14 16 18 200
10
20
30
40
50
60
70
80
90
100Queue Behavior in RED
Time (seconds)Q
ueue
(P
acke
ts)
Instantaneous QueueAverage Queue
0 2 4 6 8 10 12 14 16 18 200
10
20
30
40
50
60
70
80
90
100Queue Behavior in Drop Tail
Time (seconds)
Que
ue (
Pac
kets
)
9
Queue Behavior in RED (2)
20 new flows every 20 seconds
Wq = 0.01 Wq = 0.001
0 10 20 30 40 50 60 70 80 90 1000
10
20
30
40
50
60
70
80Queue Behavior in RED
Time (seconds)
Que
ue (
Pac
kets
)
Instantaneous QueueAverage Queue
0 10 20 30 40 50 60 70 80 90 1000
10
20
30
40
50
60
70
80
90
100Queue Behavior in RED
Time (seconds)
Que
ue (
Pac
kets
)
Instantaneous QueueAverage Queue
10
Other AQM’s Schemes
Adaptive RED, REM, GREEN, BLUE,… Problems:
Over-parameterization Not easy to implement in routers Not much better performance than drop
tail
11
REM vs. RED
0 2 4 6 8 10 12 14 16 18 200
10
20
30
40
50
60
70
80
90
100Queue Behavior in REM
Time (seconds)
Que
ue (
Pac
kets
)
0 2 4 6 8 10 12 14 16 18 200
10
20
30
40
50
60
70
80
90
100Queue Behavior in RED
Time (seconds)
Que
ue (
Pac
kets
)
Instantaneous QueueAverage Queue
12
Diffusion Mechanisms for AQM
Instantaneous queue size. Better packet marking strategy. Simplified parameters.
13
Error Diffusion Packet marking is analogous to halftoning:
Convert a continuous gray-scale image into black or white dots
Packet marking reduces to quantization Error diffusion: The error between input
(continuous) and output (discrete) is incorporated in subsequent outputs.
P[n] is the drop probability
D n6@
=1 " Packet drop (marked)0 " Packetnotmarked
'
14
Diffusion Mechanism
D[n] =1, if (P [n] - Pe[n]) H 2P [n]0, otherwise
)
Pe[n]= bi$De[n - i]
i= 1
M
!
De[n] =(P [n] +Pe[n]) - D[n]
Where:
15
Probability of Marking a Packet
Gentle RED function closely follows:
P [n] / P (qn) = Sqncma
(A)
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Evolution of the Congestion Window
TCP in steady state:
PacketsBetweenDrops= 83W2
p1 = 8
3W2
(B)
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Traffic in the Network
Congestion Window = Packets In The Pipe + Packets In The Queue
Or:
43W $N= MSS
B $RTT +qd(C)
From (A), (B), (C), and knowing that: RTT=D+q$ BMSS
P (q) = Sqcma
$N2 a=Log S
qd; ELog 2
3: D- 2 $Log MSS
B $D +2 $qd; D
where
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Probability Function
P (q) = Sqcma
$N2, if q>S$ N2h1/a
1 , otherwise
*
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1P(Q/S, N)
Q/S
P(Q
/S,
N)
N = 1N = 5N = 10
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Significant Flows
0 flows in timeout Ef = 1 Some flows in timeout Ef = (0.8 ~ 1) Most of the flows in timeout. Ef 1/N
Significant Flows=Efficiency#Flows
If number of flows exceeds capacity, then some of the flows timeout
20
Algorithm Summary
D[n] =1, if (P [n] - Pe[n]) H 2P [n]0, otherwise
)
P (q) = Sqcma
$(N $Ef)2, if q>S$(N $Ef)2_ i1/a
1 , otherwise
*
a=Ln S
qd; ELn 2
3: D- 2 $Ln MSS
B $D +2 $qd; D
• Diffusion Early Marking decides whether to mark a packet or not as:
Where:
Pe[n]= bi$De[n - i]
i= 1
M
!
De[n] =(P [n] +Pe[n]) - D[n]
M=2, b1=2/3, b2=1/3
Remember:
21
Number of Flows
The number of significant flows:
N kh=
qd+ MSSB $D
q kh
+ MSSB $DJ
L
KKK
N
P
OOO$N k - 1^h
0 10 20 30 40 50 60 70 80 90 1000
1
2
3
4
5
6
7Significant Flows
Time (seconds)
Num
ber
of
Flo
ws
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Stability of the Queue
100 long lived connections (TCP/Reno, FTP) Desired queue size = 30 packets
0 2 4 6 8 10 12 14 16 18 200
10
20
30
40
50
60
70
80
90
100Queue Behavior in RED
Time (seconds)
Que
ue (
Pac
kets
)
Instantaneous QueueAverage Queue
0 2 4 6 8 10 12 14 16 18 200
10
20
30
40
50
60
70
80
90
100Diffusion Early Marking Queue
Time (seconds)
Que
ue (
Pac
kets
)
Instantaneous QueueAverage Queue
23
Changing the number of flows
20 new flows every 20 seconds
0 10 20 30 40 50 60 70 80 90 1000
10
20
30
40
50
60
70
80
90
100Queue Behavior in RED
Time (seconds)
Que
ue (
Pac
kets
)
Instantaneous QueueAverage Queue
0 10 20 30 40 50 60 70 80 90 1000
10
20
30
40
50
60
70
80
90
100Diffusion Early Marking Queue
Time (seconds)
Que
ue (
Pac
kets
)
Instantaneous QueueAverage Queue
24
Conclusions and Future Work
Queue length stabilized and controlled without adjusting parameters.
Diffusion mechanism improves the behavior of the proposed AQM scheme.
Future Work: Optimize the estimation of parameters Analyze more traffic scenarios Complete the performance measures: fairness,
throughput Compare with other AQMs Use diffusion mechanism in other AQMs
