achieving network optima using stackelberg routing strategies
DESCRIPTION
Achieving Network Optima Using Stackelberg Routing Strategies. Yannis A. Korilis, Member, IEEE Aurel A. Lazar, Fellow, IEEE & Ariel Orda, Member IEEE IEEE/ACM transactions on networking, vol. 5, No. 1, February 1997 Sanjeev Kohli EE 228A. Presentation Outline. - PowerPoint PPT PresentationTRANSCRIPT
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Achieving Network Optima Using Stackelberg Routing Strategies
Yannis A. Korilis, Member, IEEE
Aurel A. Lazar, Fellow, IEEE
&
Ariel Orda, Member IEEEIEEE/ACM transactions on networking, vol. 5, No. 1, February 1997
Sanjeev Kohli
EE 228A
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Presentation Outline
Introduction to non cooperative networks Overview of approach Model and Problem Formulation Non cooperative User & Manager Single Follower Stackelberg Routing game Multi Follower Stackelberg Routing game Issues
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Non-cooperative Networks
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Non-cooperative Networks
Users take control decisions individually to max own performance
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Non-cooperative Networks
Users take control decisions individually to max own performance
Similar to non cooperative games
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Non-cooperative Networks
Users take control decisions individually to max own performance
Similar to non cooperative games Operating points of such networks are determined by Nash equilibria
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Non-cooperative Networks
Users take control decisions individually to max own performance
Similar to non cooperative games Operating points of such networks are determined by Nash equilibria Nash Equilibria – Unilateral deviation doesn’t
help any user
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Non-cooperative Networks
Users take control decisions individually to max own performance
Similar to non cooperative games Operating points of such networks are determined by Nash equilibria Nash Equilibria – Unilateral deviation doesn’t
help any user Inefficient, leads to sub optimal performance
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Non-cooperative Networks
Users take control decisions individually to max own performance
Similar to non cooperative games Operating points of such networks are determined by Nash equilibria Nash Equilibria – Unilateral deviation doesn’t
help any user Inefficient, leads to sub optimal performance Better solution needed !
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Network Manager
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Network Manager
Architects the n/w to achieve efficient equilibria
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Network Manager
Architects the n/w to achieve efficient equilibria Run time phase
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Network Manager
Architects the n/w to achieve efficient equilibria Run time phase Awareness of users behavior
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Network Manager
Architects the n/w to achieve efficient equilibria Run time phase Awareness of users behavior Aims to improve overall system performance
through maximally efficient strategies
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Network Manager
Architects the n/w to achieve efficient equilibria Run time phase Awareness of users behavior Aims to improve overall system performance
through maximally efficient strategies Maximally efficient strategy
Optimizes overall performance
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Network Manager
Architects the n/w to achieve efficient equilibria Run time phase Awareness of users behavior Aims to improve overall system performance
through maximally efficient strategies Maximally efficient strategy
Optimizes overall performance Individual users are well off at this operating point [Pareto Efficient]
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Presentation Outline
Introduction to non cooperative networks Overview of approach Model and Problem Formulation Non cooperative User & Manager Single Follower Stackelberg Routing game Multi Follower Stackelberg Routing game Issues
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Overview of this approach
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Overview of this approach
Total flow: Flow of users + Flow of manager
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Overview of this approach
Total flow: Flow of users + Flow of manager Example of manager’s flow
• Traffic generated by signaling/control mechanism
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Overview of this approach
Total flow: Flow of users + Flow of manager Example of manager’s flow
• Traffic generated by signaling/control mechanism
• Users traffic that belongs to virtual networks
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Overview of this approach
Total flow: Flow of users + Flow of manager Example of manager’s flow
• Traffic generated by signaling/control mechanism
• Users traffic that belongs to virtual networks
Manager optimizes system performance by controlling its portion of flow
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Overview of this approach
Total flow: Flow of users + Flow of manager Example of manager’s flow
• Traffic generated by signaling/control mechanism
• User traffic that belongs to virtual networks
Manager optimizes system performance by controlling its portion of flow Investigates manager’s role using routing as a
control paradigm
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Non Cooperative Routing Scenario
IPv4/IPv6 allow source routing
• User determines the path its flow follows from source-destination
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Goal of Manager
Optimize overall network performance according to some system wide efficiency criterion
Capability of Manager
It is aware of non cooperative behavior of users and performs its routing based on this information
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Central Idea
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Central Idea
Manager can predict user responses to its routing strategies
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Central Idea
Manager can predict user responses to its routing strategies
Allows manager to choose a strategy that leads of optimal operating point
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Central Idea
Manager can predict user responses to its routing strategies
Allows manager to choose a strategy that leads of optimal operating point
Example of Leader-Follower Game [Stackelberg]
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MAN
Org1
Org2 Org n
VP’s k
VP’s kVP’s k
User 1
User 2
User 3
User p
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Need to derive
A necessary and sufficient condition that guarantees that the manager can enforce an equilibrium that coincides with the network optimum
Above condition requires –Manager’s flow Control > Threshold
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Need to derive
A necessary and sufficient condition that guarantees that the manager can enforce an equilibrium that coincides with the network optimum
Above condition requires –Manager’s flow Control > Threshold
If the above criterion is met, we can show that the maximally efficient strategy of manager is unique and we will specify its structure explicitly
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Presentation Outline
Introduction to non cooperative networks Overview of approach Model and Problem Formulation Non cooperative User & Manager Single Follower Stackelberg Routing game Multi Follower Stackelberg Routing game Issues
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Model and Problem Formulation
User set I = {1,…..,I} Communication Links L= {1,.....,L}
Source Destination
1
2
L
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Model and Problem Formulation (contd)
Manager is referred at user 0 I0 = I U {0}
cl = capacity of link l
c = (c1,….cL) : capacity configuration
C = lL cl : total capacity of the systemof parallel links
c1 >= c2 >= …. >= cL
Each i I0 has a throughput demand ri > 0
r1 >= r2 >= …. >= rI r = iI ri
R = r + r0 Demand is less than capacity of links R < C
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Model and Problem Formulation (contd)
User i I0 splits its demand ri over the set of parallel links to send its flow
Expected flow of user i on link l is fli
Routing strategy of user i fi = (f1i,….fL
i)
Strategy space of user i Fi = {fi IRL : 0 <= fl
i <= cl, l L; lL fl
i = ri}
Routing strategy profile f = {f0, f1,….,fI) System strategy space F = iIo Fi
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Model and Problem Formulation (contd)
Cost function quantifying GoS of user i’s flow isJi : F IR i I0
Cost of user i under strategy profile f is Ji(f) Ji(f) = lL fl
iTl(fl); Tl(fl) is the average delay on link l, depends only on the total flow fl = iIo fl
i on that link
Tl(fl) = (cl - fl)-1, fl < cl
= , fl >= cl
Total cost J(f) = iIo Ji(f) = lL fl / (cl - fl)
Higher cost lower GoS provided to the user’s flow, higher average delay
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Model and Problem Formulation (contd)
is a convex function of (f1, …, fL)
a unique link flow configuration exists – min cost
(f1*,….fL
*) ;
Above is solution to classical routing opt problem, routing of all flow (users+manager) is centrally
controlled; referred to as network optimum.
1)( lll l fcf
Rffl ll ** &0
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Kuhn – Tucker Optimality conditions
(f1*,….fL
*) is the network optimum if and only if there exists a Lagrange Multiplier , such that for every link l L*
0 if )(
*2
*
lll
l ffc
c
0 if 1 ** l
l
fc
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Presentation Outline
Introduction to non cooperative networks Overview of approach Model and Problem Formulation Non cooperative User & Manager Single Follower Stackelberg Routing game Multi Follower Stackelberg Routing game Issues
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Non cooperative users
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Non cooperative users
Each user tries to find a routing strategy fi Fi that minimizes its cost Ji (average time delay)
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Non cooperative users
Each user tries to find a routing strategy fi Fi that minimizes its cost Ji (average time delay) This minimization depends on strategies of the manager and
other users, described by strategy profilef-i = (f0 , f1 ,… fi-1, fi+1 ,… fI )
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Non cooperative users
Each user tries to find a routing strategy fi Fi that minimizes its cost Ji (average time delay) This minimization depends on strategies of the manager and
other users, described by strategy profilef-i = (f0 , f1 ,… fi-1, fi+1 ,… fI )
Routing strategy of manger is FIXED f0
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Non cooperative users
Each user tries to find a routing strategy fi Fi that minimizes its cost Ji (average time delay) This minimization depends on strategies of the manager and
other users, described by strategy profilef-i = (f0 , f1 ,… fi-1, fi+1 ,… fI )
Routing strategy of manger is FIXED f0
Each user adjusts its strategy to other users actions
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Non cooperative users
Each user tries to find a routing strategy fi Fi that minimizes its cost Ji (average time delay) This minimization depends on strategies of the manager and
other users, described by strategy profilef-i = (f0 , f1 ,… fi-1, fi+1 ,… fI )
Routing strategy of manger is FIXED f0
Each user adjusts its strategy to other users actions Can be modeled as a non cooperative game, any operating
point is Nash Equilibrium; dependent on f0 !
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Non cooperative users
Each user tries to find a routing strategy fi Fi that minimizes its cost Ji (average time delay) This minimization depends on strategies of the manager and
other users, described by strategy profilef-i = (f0 , f1 ,… fi-1, fi+1 ,… fI )
Routing strategy of manger is FIXED f0
Each user adjusts its strategy to other users actions Can be modeled as a non cooperative game, any operating
point is Nash Equilibrium; dependent on f0 ! From users view point, manager reduces capacity on each
link l by fl0 , the system reduces to a set of parallel links
with capacity configuration c – f0 has a unique Nash Equilibrium
f0 f -0 ……. N0(f0)
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Non cooperative users
For a given strategy profile f-i of other users in I0, the cost of i, Ji(f) = lL fl
iTl(fl), is a convex fn of its strategy fi , hence the following min problem has a unique solution
IifgJf iii
F
i
i
),,(min arg
ig
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Kuhn – Tucker Optimality conditions
fi is the optimal response of user i if and only if there exists a (Lagrange Multiplier) , such that for every link l L,
we have
i
0 if , )(
1
0 if , )( 2
il
ll
i
il
ll
illli
ffc
ffc
ffc
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Non cooperative users
f-0 F-0 is a Nash Equilibrium of the self optimizing users induced by strategy f0 of the manger.
The function N0 : F0 F-0 that assigns the induced equilibrium of the user routing game (to each strategy of the manger) is called the Nash Mapping. It is continuous.
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Role of the Manager
It has knowledge of non cooperative behavior of users; determines the Nash Equilibrium N0(f0) induced by any routing strategy it f0 chosen by him
Acts as Stackelberg leader, that imposes its strategy on the self optimizing users that behave as followers
Aims to optimize the overall network performance, plays a social rather than selfish role
To find f0 such that if f-0 = N0(f0), then iIo fli = fl
* for all lThis f0 is called maximally efficient strategy of
manager
It is Pareto efficient !
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Outline of Results
In case of a single user, the manager can always enforce network optimum; its MES is specified explicitly
In case of any no of users, the manager can enforce the network optimum iff its demand is higher that some
threshold r0, in which case the MES is specified explicitly r0 is feasible if total demand of users plus r0 is less than C It is easy for manager to optimize heavily loaded networks
as r0 is small As the no of user increases, threshold increases i.e. harder
for manager to enforce network optimum The higher the difference in throughput demands of any two
users, the easier it is for manager to enforce network optimum
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Network optimum: (f1*,….fL
*)
Flow on link l, fl* is decreasing in link no l L
There exists some link L*, such that fl* > 0 for l <= L* and
fl* = 0 for l > L* ; L* is determined by (from [1] & [2]),
where
and G1=0, GL+1=ln=1cn = C
cl >= cl+1 Gl <= Gl+1
1**
LLGRG
LlcccGl
nnl
l
nnl ,...,2
1
1
1
1
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Using Lagrange Multiplier’s equations, we get,
Network Optimum is given by [2]
1,.....,1 *11
* Llfcfc llll
*
2
* ,...,1 ,)(
LAfc
c
Al ll
Al l
**
*
1
1
*
0
, *
*
Llf
Llc
cRccf
l
L
n n
lL
n nll
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Best reply fi of user i I0 to the strategies of manager and other users, described by f-i, can be determined as network optimum for a system of parallel links with capacity
configuration (c1i,…, cL
i)
Assuming cli >= cl+1
i , l=1,…,L-1
the flow fli is decreasing in the link no l L
There exists some link Li, such that fli > 0 for l <= Li and
fli = 0 for l > Li ; The threshold Li is determined by
LlcccG
GrG
l
n
in
il
l
n
in
il
i
L
ii
L ii
,...,2 ,
where1
1
1
1
1
LlGG
rRCcGGil
il
iL
n
in
iL
i
allfor
)(,0 and
1
111
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Best reply fi of user i to strategy profile f-i of the other users in I0 is given by
Best reply doesn’t depend on detailed description of f-i but only on residual capacity cl
i seen by user on every link l L
In practice, residual capacity info can be acquired by measuring the link delays using an appropriate estimation technique
iil
i
L
m
im
ilL
m
iim
il
il
Llf
Llc
crccf i
i
,0
, )(
1
1
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Presentation Outline
Introduction to non cooperative networks Overview of approach Model and Problem Formulation Non cooperative User & Manager Single Follower Stackelberg Routing game Multi Follower Stackelberg Routing game Issues
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Single Follower Stackelberg Routing Game
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Single Follower Stackelberg Routing Game
In this game, there exists a MES of the manager then it is unique and is given by
LlHHthus
LlRH
LlcGH
RfHH
Llcc
ffH
ll
l
lll
L
n nL
l
n
l
nn
l
lnl
,...,1 ,
,
,/
,0 and
,...,2 ,
1
*
**
1
*11
1
1
1
1
**
1
1
11*0
1
1
1
1*0
11
1
1
by determined is where ,
,
LL
ll
L
n n
L
n nll
HrH
LLlff
Llc
rfcf
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Single Follower Stackelberg Routing Game
The best reply f1 of the follower is
Therefore, {1,…,L1} is the set of links over which the follower sends its flow when manager implements f0. For manager: Send flow fl
* on every link l that will not receive any flow from the follower
Split the rest of its flow among the links that willreceive user flow proportional to their capacities
1
1
1
1
1**0
1*1
,0
,1
1
Ll
Llc
rfcffff
L
n n
L
n nllll
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Presentation Outline
Introduction to non cooperative networks Overview of approach Model and Problem Formulation Non cooperative User & Manager Single Follower Stackelberg Routing game Multi Follower Stackelberg Routing game Issues
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Multi Follower Stackelberg Routing Game
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Multi Follower Stackelberg Routing Game
An arbitrary number I of self optimizing users share the system of parallel links
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Multi Follower Stackelberg Routing Game
An arbitrary number I of self optimizing users share the system of parallel links
Maximally Efficient Strategy of manager (if it exists) and the corresponding Nash Equilibrium of non cooperative users is:
lli
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and }:{,link every for and
by determined is ,user every for where
,)1(
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Multi Follower Stackelberg Routing Game
Equilibrium strategy fi of user i I is described by
If a MES exists, then the induced Nash equilibrium of the followers has precisely the same structure with the best reply follower in the single follower case
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**
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Remarks - M F Stackelberg Routing Game
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Remarks - M F Stackelberg Routing Game
{1,…., Li} is the set of links that receive flow from follower i I
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Remarks - M F Stackelberg Routing Game
{1,…., Li} is the set of links that receive flow from follower i I
Il is the set of followers that send flow on link l. Since H1 = 0 < ri, i I, all users send flow on link 1 I1 = I
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Remarks - M F Stackelberg Routing Game
{1,…., Li} is the set of links that receive flow from follower i I
Il is the set of followers that send flow on link l. Since H1 = 0 < ri, i I, all users send flow on link 1 I1 = I
For f0 to be admissible, fl0 >= 0, for all l L
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Remarks - M F Stackelberg Routing Game
{1,…., Li} is the set of links that receive flow from follower i I
Il is the set of followers that send flow on link l. Since H1 = 0 < ri, i I, all users send flow on link 1 I1 = I
For f0 to be admissible, fl0 >= 0, for all l L
If fl0 < 0 fl-1
0 < 0
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Remarks - M F Stackelberg Routing Game
{1,…., Li} is the set of links that receive flow from follower i I
Il is the set of followers that send flow on link l. Since H1 = 0 < ri, i I, all users send flow on link 1 I1 = I
For f0 to be admissible, fl0 >= 0, for all l L
If fl0 < 0 fl-1
0 < 0
Admissible condition reduces to f10 >= 0
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Remarks - M F Stackelberg Routing Game
{1,…., Li} is the set of links that receive flow from follower i I
Il is the set of followers that send flow on link l. Since H1 = 0 < ri, i I, all users send flow on link 1 I1 = I
For f0 to be admissible, fl0 >= 0, for all l L
If fl0 < 0 fl-1
0 < 0
Admissible condition reduces to f10 >= 0
f10 is an increasing function of the throughput demand r0 of
leader, r0 [0, C - r] ………. [3]
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Theorem
There exists some r0, with 0 < r0 < C – r, such that the leader in multi follower Stackelberg routing game can enforce the network optimum if and only if its throughput demand r0
satisfies r0 < r0 < C – r. The maximally efficient strategy of leader is given by
lli
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n n
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ll
IILlIiILl
HrH
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LlfIc
rfcf
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i
i
and }:{,link every for and
by determined is ,user every for where
,)1(
1
*
1
1
*0
![Page 74: Achieving Network Optima Using Stackelberg Routing Strategies](https://reader035.vdocuments.net/reader035/viewer/2022062518/568146ea550346895db42346/html5/thumbnails/74.jpg)
Presentation Outline
Introduction to non cooperative networks Overview of approach Model and Problem Formulation Non cooperative User & Manager Single Follower Stackelberg Routing game Multi Follower Stackelberg Routing game Issues
![Page 75: Achieving Network Optima Using Stackelberg Routing Strategies](https://reader035.vdocuments.net/reader035/viewer/2022062518/568146ea550346895db42346/html5/thumbnails/75.jpg)
Properties of Leader Threshold r0
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r0 of the leader is a unique solution of the equation
“f10(r0) = 0” in r0 [0, C - r]
Properties of Leader Threshold r0
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r0 of the leader is a unique solution of the equation
“f10(r0) = 0” in r0 [0, C - r]
When r C, r0 0 i.e. in heavily loaded networks, controlling a small portion of flow can drive the system into the network optimum
Properties of Leader Threshold r0
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r0 of the leader is a unique solution of the equation
“f10(r0) = 0” in r0 [0, C - r]
When r C, r0 0 i.e. in heavily loaded networks, controlling a small portion of flow can drive the system into the network optimum
With throughput demand r fixed, the leader threshold r0 increases with increase in no of users.
Properties of Leader Threshold r0
![Page 79: Achieving Network Optima Using Stackelberg Routing Strategies](https://reader035.vdocuments.net/reader035/viewer/2022062518/568146ea550346895db42346/html5/thumbnails/79.jpg)
r0 of the leader is a unique solution of the equation
“f10(r0) = 0” in r0 [0, C - r]
When r C, r0 0 i.e. in heavily loaded networks, controlling a small portion of flow can drive the system into the network optimum
With throughput demand r fixed, the leader threshold r0 increases with increase in no of users.
Leader threshold r0 decreases with increase in difference in user demands
Properties of Leader Threshold r0
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Set of Parallel links with capacity conf (12,7,5,3,2,1), shared by I identical followers with total demand r
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Set of Parallel links with capacity conf (12,7,5,3,2,1), shared by 100 identical self optimizing users with total demand r and the manager
r0 = r0
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Set of Parallel links with capacity conf (12,7,5,3,2,1), shared by 100 identical self optimizing users with total demand r and the manager
r0 = r0
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Set of Parallel links with capacity conf (12,7,5,3,2,1), shared by 100 identical self optimizing users with total demand r and the manager
r0 = r0
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Set of Parallel links with capacity conf (12,7,5,3,2,1), shared by 100 identical self optimizing users with total demand r and the manager
r0 = r0
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Scalability
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To determine maximally efficient strategy, manager needs throughput demand ri of every user.
Scalability
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To determine maximally efficient strategy, manager needs throughput demand ri of every user.
In many networks, user declare average rate ri during negotiation phase
Scalability
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To determine maximally efficient strategy, manager needs throughput demand ri of every user.
In many networks, user declare average rate ri during negotiation phase
Alternatively, the manager can estimate average rates by monitoring the behavior of users
Scalability
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To determine maximally efficient strategy, manager needs throughput demand ri of every user.
In many networks, user declare average rate ri during negotiation phase
Alternatively, the manager can estimate average rates by monitoring the behavior of users
Manager can adjust its strategy to maximally efficient one whenever a user departs or a new one joins the network
Scalability
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To determine maximally efficient strategy, manager needs throughput demand ri of every user.
In many networks, user declare average rate ri during negotiation phase
Alternatively, the manager can estimate average rates by monitoring the behavior of users
Manager can adjust its strategy to maximally efficient one whenever a user departs or a new one joins the network
User not necessarily mean a single user, it can be a group of users joining the network as an organization. It also reduces threshold r0
Scalability
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References
[1] A. Orda, R. Rom, and N. Shimkin, “Competitive routing in multi-user communication networks,” IEEE/ACM Trans. Networking, vol. 1, pp. 510-521, Oct. 1993.
[2] Y.A. Korilis, A.A. Lazar, and A. Orda, “Capacity allocation under non cooperative routing,” IEEE Trans. Automat. Contr.
[3] Y.A. Korilis, A.A. Lazar, and A. Orda, “Achieving network optima using Stackelberg routing strategies,” Center for
Telecommunications Research, Columbia University, NY, CTR Tech. Rep. 384-94-31, 1994.
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THANK YOU