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Transcript of Farsighted Congestion Controllers Milan Vojnović Microsoft Research Cambridge, United Kingdom...
Farsighted Congestion Controllers
Milan Vojnović
Microsoft Research
Cambridge, United KingdomCollaborators:Dinan Gunawardena (MSRC), Peter Key (MSRC), Shao Liu (UIUC), Laurent Massoulié (MSRC)
MIT, 09 Nov 05
2
Problem
Applications concerned with long-run throughput Indifferent to short-timescale throughput Ex. peer-to-peer file sharing
Goal: Optimize long-run throughput Share bandwidth fairly with TCP
Data transfer
WebWeb Internet
3
0 0.5 1 1.5 2
x 104
0
1
2
3
4
5
6
x 106
time
rates
rates over time, FAR and TCP
FAR
TCP
Solution
Number ofconnections
Farsighted TCP
TCPTCP
Rat
e (M
b/s)
Internet
Time
4
Solution: farsighted controller
w w + 1/ww max(w – 1/(ww0)
+ ack- ack
-m
Window
Time
high congestion
• Two-timescale control• = parameter learned on-line at slow timescale
w0
5
Compare with TCP
Window
Time
w w + 1/ww w – ½ w
+ ack- ack
high congestion
6
Roadmap
Optimality Properties Rate adaptation Protocol & verification Conclusion
7
Setup
Network state fluctuates over a set of phases U
Ex. single link phase = number of competing flows
(u) = fraction of time phase is u Cl,u(x) = cost of link l with arrival rate x
Network
8
Setup (cont’d)
Vr(x) = utility for rate x = (x(u), uU)
User r
Uu
rrrr uxUuxV ))(()()(
rrr xxU / const )(
)()( rrrr xUxV
Uu
rr uxux )()(
TCP-like Long-run throughput optimizer
9
Problem
l ql
qulUuRr
rr uxCuxV ))(()()( ,
0)(uxr
maximize
over
SYSTEM:
Rrxr , optimal if it solves SYSTEM
10
TCP-like only
l ql
qulRr
rr uxCuxUu
))(())(( ,
0)(uxr
maximize
over
• Separation into independent problems
• Traditional controllers are “myopic”• Optimize rates “independently over time”
SYSTEM u:
11
With long-run throughput optimizers
l qlqul
Uu
Frrr
Mrrr
uxCu
xUuxUu
))(()(
)())(()(
,
0)(uxr
maximize
over
• No separation
• Long-run throughput optimizers = “farsighted”
SYSTEM:
12
Formally: multi-path problem
phase 1 phase 2 phase 3 phase N. . .
rxr(1) xr(2) xr(3) xr(N)
Studied by Gibbens & Kelly 02
But our setup in phase spacePath is not spatial path present at all times “Paths come and leave over time”Time (not space) diversity
13
Roadmap
Optimality Properties Rate adaptation Protocol & verification Conclusion
14
Price equalization
Farsighted user r pr(u) = price when phase is u (price = loss event rate)
rr
rrr
pup
pupux
)( else,
)( ,0)( If
)(' rrr xUp
“good phase”
“bad phase”
“reference price”
15
Special: single link
farsightedmyopic 1
u
Phase u = u competing myopic flows
xF(u)xM(u)
else
)()(
0
1 uuuxuxF
else)(
u
uuxuxM 1
)),max()(()( '' u
FM uxuUxU 01
1 uxu : integer largest
capacity = 1
16
Farsighted users are conservative
A flow said r to be conservative iff
= average user-perceived price
)(1'
rrr pUx
rp
ur
urr
r uxu
uxupup
)()(
)()()(
Seen as throughput maximizers under a “TCP-friendly” constraint
“TCP-friendly”
If TCP lossthroughput(C)
Farsighted user: “=“ in (C)
17
Throughput comparison
Consider a farsighted user F and a myopic user M
Both with same utility functions Both competing for same set of links
MF xx Result
18
Diminishing returns with switching to farsighted n flows k farsighted, n-k myopic flows use same routes = throughput of farsighted flow for given k
kkxF withdecreases )(
)(kxF
Result
19
Can be made “low-priority”
One link characterized by increasing, convex function
Strictly concave utility functions f farsighted flows (0) = fraction of time no myopic flow on the link
Result
0 all ),()( ')0(' xxUxU MfF
“low-priority” iff
20
File transfer time Short-lived flows:
Poisson arrivalsExponential file sizes
short lived
long lived myopic
S1:
short lived
long lived farsighted
S2:
21 TT Result Ti = mean file transfer time in Si
21
Roadmap
Optimality Properties Rate adaptation Protocol & verification Conclusion
22
Traditional myopic
))()(( ' dtNxdtxUxkdx rrrrrrr 212
))(( '
rl
lrrrrdtd qxUkx ql = price at link l
Fast time scale (RTT)
TCP:• 0 or 1• 1 with rate
rllr qx
const
rl
lq)('rr xU
23
Farsighted
))(( 'rrrrrdt
d xUa
1
)(
rl
lrrrdtd qkx
Fast timescale (RTT)
Slow timescale
ar small r)('rr xU
24
Roadmap
Optimality Properties Rate adaptation Protocol & verification Conclusion
25
Back to the solution
w w + 1/ww w – 1/(w
+ ack- ack
-m
Window
Time
high congestion
• Two-timescale control• = parameter learned on-line at slow timescale
26
Sensing phase
vcwnd vcwnd + 1/w0
vcwnd vcwnd – 1/(w0+ ack- ack
-m
Time
w0
Sequential hypothesis testing: p In fact, optimal for Poi(pw0) losses (CUSUM)
Know how to set m so false positives are rare and control is responsive (reflected random walk)
27
“Reference price”: initial guess Want be almost constant Solution: small gain for adaptation But need to converge to equilibrium Solution:
Initial guess = current loss rate
gain
number of iterates
g_min
g_max
n0 n1
loss rate
g_max = 0.005g_min = 0.0001
28
Verification by simulation Scenario 1:
1 2 3 4 5 6 7 8 9 100
1
2
3
4
5
6
7
phases
flow
num
ber
Pyrimid Topology, 2-6 flows
1 period has 9 phasesu = (2,3,4,5,6,5,4,3,2)
RED, 6 Mb/sLong-lived farsightedLong-lived TCP
Phase duration = 800 sec
29
Send rate
0 1000 2000 3000 4000 5000 6000 7000
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
x 106 Rate vs time, FAR and TCP
time
rate
FAR
TCP
Time (sec)
Sen
d ra
te (
Mb/
s)
FAR
TCP
30
Loss rate
0 1000 2000 3000 4000 5000 6000 70000
1
2
3
4
5
6
7
8
x 10-3 Loss event probabilities of FAR and TCP and xi of FAR
time
Pro
bability
FAR
TCPxi
Loss
rat
e
Time (sec)
FAR
TCPReference loss rate
31
Per phase rate averages
2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 70
0.5
1
1.5
2
2.5
3
3.5
4
4.5x 10
6Phase average rate: Theoretical Vs Measurement, Farsighted and TCP
Phases
Pha
se a
ve ra
tes
Far measured
Far theoreticalTCP measured
TCP theoretical
The 7th phase is theaverage value over allphases
Phase FAR (Mbps) TCP (Mbps)
2 4.38/4.24 1.61/1.73
3 2.77/2.46 1.61/1.77
4 1.15/1.20 1.61/1.58
5 0/0.62 1.50/1.33
6 0/0.23 1.20/1.11
Avg rate
1.61/1.73 1.53/1.53
Phase
FAR theory
FAR simulation
TCP simulations
TCP theory
Total Avg
Ave
rage
sen
d ra
te (
Mb/
s)
32
Scenario 2
0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2
x 104
2
3
4
5
6
7
8
time
flow
num
ber
inclu
din
g p
ers
iste
nt
flow
s
flow number over time
0 0.5 1 1.5 2
x 104
0
1
2
3
4
5
6
x 106
time
rate
s
rates over time, FAR and TCP
FAR
TCP
1 2 3 4 5 6 7 80
1
2
3
4
5
6
7x 10
6 Phase average rate: Theoretical Vs Measurement, Far and TCP
phases
phas
e av
e ra
te
FAR measurement
FAR theoreticalTCP measurement
TCP theoretical
The last phase is for theaverage of all phases
Time (sec)
Num
ber
of F
low
sS
end
Rat
e (b
/s)
Ave
rage
sen
d ra
te (
Mb/
s)
Time (sec)Phase
FAR theory
FAR simulation
TCP simulations
TCP theory
Total Avg
33
File transfer time
RED, 6 Mb/s
TCPTCP
RED, 6 Mb/s
FARTCP
Fn ~ Exp()
Tn = Poi()
S1:
S2:
= 0.11/ = 10 MB
S1 S2
Avg Flow Number 8.7139 8.1679
Avg file transfer time (sec) 179 173
Avg link bandwidth (Mb/s) 10.80 10.82
Per connection avg rate (Mb/s) TCP = 1.3405
TCP = 1.3472
FAR = 1.3642
TCP = 1.3262
34
Benefits to other flows?
Ex. same as earlier slide But 10 long-lived flows: either all TCP or all FAR
= 0.051/ = 20 MB
10 FAR 10 TCP
Avg Flow Number 6.92 12.84
Avg Transfer Time (sec) 349 470
35
More realistic traffic
Synthetic web (UNC, Jeffay+) Requests, responses,
idle times drawn from empirical distributions
S1: 1 persistent TCP S2: 1 persistent FAR
Both S1 & S2: number of web users = 1
0 100 200 300 400 500 6000
100
200
300
400
500
600
TCP
FAR
File transfer time for FAR and TCP
TCP: File transfer time (sec)
FA
R:
File
tra
nsfe
r tim
e (s
ec)
36
Conclusion
Farsighted Congestion Control Solution for long-run throughput optimization
Decentralized control No special feedback required
(standard TCP sender modif) Not a heuristic hack
Microeconomics rationale Benefits to other flows On-going:
Further simulations Experimental implementation in MS Vista Real-word experiments
37
More
http://research.microsoft.com/~milanv/farsighted.htm
& Thanks!