Group J VACATIONS Frankie Battle Evan Doyle Chris Roldan Johnera Stewart Shawna Taylor.
CS/EE netlab.CALTECH.edu with J. Doyle, L. Li, A. Tang, J ... · CS/EE netlab.CALTECH.edu July 2004...
Transcript of CS/EE netlab.CALTECH.edu with J. Doyle, L. Li, A. Tang, J ... · CS/EE netlab.CALTECH.edu July 2004...
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Internet Protocols
Steven Low
CS/EEnetlab.CALTECH.edu
July 2004
with J. Doyle, L. Li, A. Tang, J. Wang
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Internet Protocols
Selects paths from sources to dests
TCP/AQM
IP
xi(t)
pl(t)
Selects source transmission rates
Link Selects topology, capacities, power,…
Application Selects user criteria, web layout, utility
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Internet Protocols
TCP/AQM
IP
xi(t)
pl(t)
Link
ApplicationProtocols determines network behaviorCritical, yet difficult, to understand and optimizeLocal algorithms, distributed spatially and vertically global behaviorDesigned separately, deployed asynchronously, evolves independently
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Internet Protocols
TCP/AQM
IP
xi(t)
pl(t)
Link
ApplicationProtocols determines network behaviorCritical, yet difficult, to understand and optimizeLocal algorithms, distributed spatially and vertically global behaviorDesigned separately, deployed asynchronously, evolves independently
Need to reverse engineer
… much easier than biologywith full specs
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Internet Protocols
Minimize path costs (IP)
TCP/AQM
IP
xi(t)
pl(t)
Maximize utility (TCP/AQM)
Link Minimize SIR, max capacities, …
Application Minimize response time (web layout)
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Internet Protocols
Each layer is abstracted as an optimization problemOperation of a layer is a distributed solutionResults of one problem (layer) are parameters of othersOperate at different timescales
TCP/AQM
IP
Link
Application
cRx
xUi
iix
≤
∑≥
tosubj
)( max0
Application
IP Link
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IP
TCP/AQM
Applications
Link
Source coding
FAST TCP/AQM
IP routing
HOT topology
A Theory for the Internet?
General Approach:
1) Understand a single layer in isolation and assumeother layers are designed nearly optimally.
2) Understand interactions across layers
3) Incorporate additional layers, with the ultimate goal of viewing entire protocol stack as solving one giant optimization problem (where individual layers are solving parts of it).
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IP
TCP/AQM
Applications
Link
Carlson: HOT traffic & web layout
Low: equilirbiumPaganini: dynamics
Low: routing
Willinger: HOT topology
Outline
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IP
TCP/AQM
Applications
Link
Outline
Summary DetailsChallenges
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Network model
F1
FN
G1
GL
R
RT
TCP Network AQM
x y
q p
))( ),(( )1())( ),(( )1(
tRxtpGtptxtpRFtx T
=+=+ Reno, Vegas
DT, RED, …
liRli link uses source if 1= IP routing
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Protocol decomposition
BccRx
xU
Ti
iixRc
≤≤
∑≥
α , subject to
)( maxmax max0
TCP-AQM
TCP algorithms maximize utility with different utility functions
Congestion prices coordinate across protocol layers
(Kelly, Malloo, Tan 98)
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Protocol decomposition
BccRx
xU
Ti
iixRc
≤≤
∑≥
α , subject to
)( maxmax max0
TCP-AQMIP
TCP algorithms maximize utility with different utility functionsIP shortest path routing is optimal using congestion prices as link costs, with given link capacities c
Congestion prices coordinate across protocol layers
(Wang, Li, Low, Doyle 03)
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Protocol decomposition
BccRx
xU
Ti
iixRc
≤≤
∑≥
α , subject to
)( maxmax max0
TCP-AQMIPLink
TCP algorithms maximize utility with different utility functionsIP shortest path routing is optimal using congestion prices as link costs, with given link capacities cWith optimal provisioning, static routing is optimal using provisioning cost α as link costs
Congestion prices coordinate across protocol layers
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Protocol decomposition – TCP/AQM
BccRx
xU
Ti
iixRc
≤≤
∑≥
α , subject to
)( maxmax max0
TCP-AQM
TCP/AQM: TCP maximizes aggregate utility (not throughput)Fair bandwidth allocation is not always inefficient Increasing capacity does not always raise throughput
Intricate network interactions paradoxical behavior
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Protocol decomposition – TCP/IP
BccRx
xU
Ti
iixRc
≤≤
∑≥
α , subject to
)( maxmax max0
TCP-AQMIP
TCP/IP (fixed c): Equilibrium exists iff zero duality gapNP-hard, but subclass with zero duality gap is LP Equilibrium, if exists, can be unstableCan stabilize, but with reduced utility
Inevitable tradeoff bw utility max & routing stability
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IP
TCP/AQM
Applications
Link
Outline
SummaryDetails
TCPTCP/IP
Challenges
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Duality modelFlow control problem (Kelly, Malloo, Tan 98)
TCP/AQMMaximize utility with different utility functions(L 03): (x*,p*) primal-dual optimal iff
Primal-dual algorithm
Reno, Vegas, FAST
DT, RED, REM/PI, AVQ
cRx
xU ii
ix
≤
∑≥
t.s.
)( max0
),()1(),( )1(
RxpGtppRxFtx T
=+=+
0 ifequality with ** >≤ lll pcy
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Duality model
Historically, packet level implemented firstFlow level understood as after-thoughtBut flow level design determines
performance, fairness, stability
α = 1 : Vegas, FAST, STCP α = 1.2: HSTCP (homogeneous sources)
α = 2 : Reno (homogeneous sources)
α = infinity: XCP (single link only)
⎩⎨⎧
=≠−
=−
1 if log1 if )1( )(
1
ααα α
i
iii x
xxU
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Duality model
Historically, packet level implemented firstFlow level understood as after-thoughtBut flow level design determines
performance, fairness, stability
NowGiven (application) utility functions, can generate provably scalable TCP algorithms (Paganini)
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Questions
Is fair allocation always inefficient
Does raising capacity always increase throughput
Intricate and surprising interactions in network … unlike at single-link ……
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Questions
Is fair allocation always inefficient
Does raising capacity always increase throughput
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Fairness
Identify allocation with αAn allocation is fairer if its α is larger
cRx
xU ii
ix
≤
∑≥
t.s.
)( max0
(Mo, Walrand 00)
⎩⎨⎧
=≠−
=−
1 if log1 if )1( )(
1
ααα α
i
iii x
xxU
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Fairness
α = 0: maximum throughput α = 1: proportional fairness α = 2: min delay fairness (Reno) α = infinity: maxmin fairness
cRx
xU ii
ix
≤
∑≥
t.s.
)( max0
⎩⎨⎧
=≠−
=−
1 if log1 if )1( )(
1
ααα α
i
iii x
xxU
(Mo, Walrand 00)
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Fairness
α = 1 : Vegas, FAST, STCP α = 1.2: HSTCP (homogeneous sources)
α = 2 : Reno (homogeneous sources)
α = infinity: XCP (single link only)
cRx
xU ii
ix
≤
∑≥
t.s.
)( max0
⎩⎨⎧
=≠−
=−
1 if log1 if )1( )(
1
ααα α
i
iii x
xxU
(Mo, Walrand 00)
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Efficiency
Unique optimal rate x(α) An allocation is efficient if T(α) is large
cRx
xU ii
ix
≤
∑≥
t.s.
)( max0
⎩⎨⎧
=≠−
=−
1 if log1 if )1( )(
1
ααα α
i
iii x
xxU
∑=i
ixT )(:)( throughput αα
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Conjecture
ConjectureT(α) is nonincreasing
i.e. a fair allocation is always inefficient
cRx
xU ii
ix
≤
∑≥
t.s.
)( max0
⎩⎨⎧
=≠−
=−
1 if log1 if )1( )(
1
ααα α
i
iii x
xxU
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Example 1
ConjectureT(α) is nonincreasing
i.e. a fair allocation is always inefficient
1=lc0
max throughput
1
1/(L+1)
proportionalfairness
L/L(L+1)
)()1( )0( ∞>> TTT
1/2
maxminfairness
1/2
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Example 2
21 cc ≤⎟⎠⎞⎜
⎝⎛ −+++= 21
22
21213
2)1( ccccccT
2)( 2
1ccT +=∞
)()1( ∞>⇒ TT
ConjectureT(α) is nonincreasing
i.e. a fair allocation is always inefficient
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Example 3
↓)(αT
ConjectureT(α) is nonincreasing
i.e. a fair allocation is always inefficient
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Intuition
“The fundamental conflict between achieving flow fairness and maximizing overall system throughput….. The basic issue is thus the trade-off between these two conflicting criteria.”
Luo,etc.(2003), ACM MONET
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Results
Theorem: Necessary & sufficient condition for general networks (R, c) provided every link has a 1-link flow
Corollary 1: true if N(R)=1
1/1
/1
+α
α
LL
11
/1 +αL1=lc
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Results
Theorem: Necessary & sufficient condition for general networks (R, c) provided every link has a 1-link flow
Corollary 2: true ifN(R)=22 long flows pass through same# links
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Counter-example
There exists a network such that
dT/dα > 0 for almost all α>0Intuition
Large α favors expensive flowsLong flows may not be expensive
Max-min may be more efficient than proportional fairness
expensive long
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Counter-example
Theorem: Given any α0>0, there exists network where
Compact example
0α
0 allfor 0 ααα
>>ddT
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Questions
Is fair allocation always inefficeint
Does raising capacity always increase throughput
Intricate and surprising interactions in network … unlike at single-link ……
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Throughput & capacity
Intuition: Increasing link capacities always raises throughput T
Theorem: Necessary & sufficient condition for general networks (R, c)
Corollary: For all α, increasinga link’s capacity can reduce Tall links’ capacities equally can reduce Tall links’ capacities proportionally raises T
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IP
TCP/AQM
Applications
Link
Outline
SummaryDetails
TCPTCP/IP
Challenges
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Motivation
⎟⎟⎠
⎞⎜⎜⎝
⎛+⎟
⎠
⎞⎜⎝
⎛−
≤
∑∑ ∑
∑
≥≥
≥
ll
li l
lliRiiixp
iii
xR
cppRxxU
cRxxU
ii
max)( max min
subject to )( maxmax
00
0
:Dual
:Primal
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Motivation
Can TCP/IP maximize utility?
⎟⎟⎠
⎞⎜⎜⎝
⎛+⎟
⎠
⎞⎜⎝
⎛−
≤
∑∑ ∑
∑
≥≥
≥
ll
li l
lliRiiixp
iii
xR
cppRxxU
cRxxU
ii
max)( max min
subject to )( maxmax
00
0
:Dual
:Primal
Shortest path routing!
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Two timescales
Instant convergence of TCP/IPLink cost = a pl(t) + b dl
Shortest path routing R(t)price
static
TCP/AQM
IPR(0)
a p(0)
R(1)
a p(1)
… R(t), R(t+1) ,…
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Questions
Does equilibrium routing Ra exist ?What is utility at Ra?Is Ra stable ? Can it be stabilized?
TCP/AQM
IPR(0)
a p(0)
R(1)
a p(1)
… R(t), R(t+1) ,…
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Equilibrium routing
Theorem1.If b=0, Ra exists iff zero duality gap
Shortest-path routing is optimal with congestion pricesNo penalty for not splitting
⎟⎟⎠
⎞⎜⎜⎝
⎛+⎟
⎠
⎞⎜⎝
⎛−
≤
∑∑ ∑
∑
≥≥
≥
ll
li l
lliRiiixp
iii
xR
cppRxxU
cRxxU
ii
max)( max min
subject to )( maxmax
00
0
:Dual
:Primal
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ProofReduce integer partition to primal problemGiven: integers {c1, …, cn}Find: set A s.t.
Equilibrium routing
Theorem1.If b=0, Ra exists iff zero duality gap
Shortest-path routing is optimal with congestion pricesNo penalty for not splitting
2. Primal problem is NP-hardSubclass of problems with zero gap is LP
∑∑∉∈
=Ai
iAi
i cc
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TCP/IP
Theorem1.If b=0, Ra exists iff zero duality gap
Shortest-path routing is optimal with congestion pricesNo penalty for not splitting
2. Primal problem is NP-hardSubclass of problems with zero gap is LP
3. Tradeoff between routing stability and achievable utility
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TCP/IP/provisioning
Theorem1.If b=0, Ra exists iff zero duality gap
Shortest-path routing is optimal with congestion pricesNo penalty for not splitting
2. Primal problem is NP-hardSubclass of problems with zero gap is LP
3. Tradeoff between routing stability and achievable utility
4. Static routing is optimal if capacity c is optimally provisioned
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Protocol decomposition
BccRx
xU
Ti
iixRc
≤≤
∑≥
α , subject to
)( maxmax max0
TCP-AQMIPLink
TCP algorithms maximize utility with different utility functionsIP shortest path routing is optimal using congestion prices as link costs, with given link capacities cWith optimal provisioning, static routing is optimal using provisioning cost α as link costs
Congestion prices coordinate across protocol layers
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IP
TCP/AQM
Applications
Link
Separation theorem
Heterogeneous protocols
• Duality & complexity• Routing dynamics
• Integration of topology• … wireless ad hoc
Challenges
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IP
TCP/AQM
Applications
Link
Separation theorem
Heterogeneous protocols
• Duality & complexity• Routing dynamics
• Integration of topology• … wireless ad hoc
Challenges
What is the ultimateunderlying problem?
Integration of control, communication,
computing?(nonlinear, distributed, delayed,
random, …)
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IP
TCP/AQM
Applications
Link
Separation theorem
Heterogeneous protocols
• Duality & complexity• Routing dynamics
• Integration of topology• … wireless ad hoc
Challenges
Are these the right questions?
(scalability, evolvability, verifiability, fragility,
simplicity, …)