Flows and Networks (158052)

Flows and Networks (158052)

Richard BoucherieStochastische Operations Research -- TW

wwwhome.math.utwente.nl/~boucherierj/onderwijs/158052/158052.html

Introduction to theorie of flows in complex networks: both stochastic and deterministic apects

Size5 ECTS

16 lectures : 8 by R.J. Boucherie focusing on stochastic

networks 8 by W. Kern focusing on deterministic

networks

Common problemHow to optimize resource allocation so as to

maximize flow of items through the nodes of a complex network

Material: handouts / downloads

Exam: exercises / (take home) exam

References: see website

http://wwwhome.math.utwente.nl/~boucherierj/onderwijs/158052/153087.html




Motivation and main question

Motivation

Production / storage system

C:\Flexsim Demo\tutorial\Tutorial 3.fsm

Internet Thomas Bonalds's animation of TCP.htm (www-sop.inria.fr/mistral/personnel/Thomas.Bonald/tcp_eng.html)

http://www.warriorsofthe.net/

trailer

Main questions

How to allocate servers / capacity to nodes orhow to route jobs through the systemto maximize system performance, such as throughput, sojourn time, utilization

QUESTIONS

http://www.warriorsofthe.net/

Aim: Optimal design of Jackson network

• Consider an open Jackson network

with transition rates

• Assume that the service rates and arrival rates

are given

• Let the costs per time unit for a job residing at queue j be

• Let the costs for routing a job from station i to station j be

• (i) Formulate the design problem (allocation of routing

probabilities) as an optimisation problem.

• (ii) Provide the solution to this problem

kk

jjj

jkjjk

pnTnq

pnTnq

pnTnq

000

00

))(,(

))(,(

))(,(

ja

jkb

0j

Flows and network: stochastic networks

Contents

1. Introduction; Markov chains

2. Birth-death processes; Poisson process, simple queue;reversibility; detailed balance

3. Output of simple queue; Tandem network; equilibrium distribution

4. Jackson networks;Partial balance

5. Sojourn time simple queue and tandem network

6. Performance measures for Jackson networks:throughput, mean sojourn time, blocking

7. Application: service rate allocation for throughput optimisationApplication: optimal routing

Today:

• Introduction / motivation course• Discrete-time Markov chain• Continuous-time Markov chain• Next• Exercises

Today:


• AEX• Continuous, per minute, per day• Random process: reason increase / decrease ?

• Probability level 300 of 400 dec 2004 ? • Given level 350 : buy or sell ?

• Markov chain : random walk

Gambler’s ruin

• Gambling game: on any turn– Win €1 w.p. p=0.4– Lose €1 w.p. 1-p=0.6 – Continue to play until €N – If fortune reaches €0 you must stop

– Xn= amount after n plays

– For

– Xn has the Markov property: conditional probability that given the entire history depends only on

– Xn is a discrete time Markov chain

4.0

),,...,|1( 00111

iXiXiXiXP nn

001111 ,...,, iXiXiXiX nnn

)|1( 1 iXiXP nn

0011 ...,, iXiXiX nnn

jX n 1

iX n

Markov chain

• Xn is time-homogeneous

• Transition probability• State space : all possible states

• For gambler’s ruin

• For N=5: transition matrix

• Property

)|(),( 1 iXjXPjip nn

},...,1,0{ NS

1),(,1)0,0(

0;6.0)1,(;4.0)1,(

NNpp

Niiipiip

0.100000

4.006.0000

04.006.000

004.006.00

0004.006.0

000000.1

P

Nijip

NjijipN

j

0,1),(

,0,0),(

0

Markov chain : equilibrium distribution

• n-step transition probability

• Evaluate:

• Chapman-Kolmogorov equation

• n-step transition matrix

• Initial distribution

• Distribution at time n

• Matrix form

),()|()|( 0 jiPiXjXPiXjXP nnmnm

),(),(),(1

2 jkpkipjiPN

k

),(),(),(1

jkPkiPjiP mn

N

kmn

),)((),( jiPjiPPP nn

nn

)()( 0 iqiXP ))(),..,1(( Nqqq

),()()()(1

jkPkqjXPjp n

N

knn

nnnn PNpp qp ))(),..,1((

Markov chain: classification of states

• j reachable from i if there exists a path from i to j• i and j communicate when j reachable from i and

i reachable from j • State i absorbing if p(i,i)=1• State i transient if there exists j such that j

reachable from i and i not reachable from j • Recurrent state i process returns to i infinitely

often = non transient state• State i periodic with period k>1 if k is smallest

number such that all paths from i to i have length that is multiple of k

• Aperiodic state: recurrent state that is not periodic

• Ergodic Markov chain: alle states communicate, are recurrent and aperiodic (irreducible, aperiodic)

2.08.0000

1.04.05.000

07.03.000

0005.05.0

0006.04.0

P

Boucherie

plaatjes uit boek op bord tekenen!

Markov chain : equilibrium distribution

• Assume: Markov chain ergodic

• Equilibrium distribution

independent initial state

stationary distribution

• normalising

interpretation probability flux

)(),(lim)|(lim 0 jjiPiXjXP nn

nn

)(),(),(1 jjiPjiP nn

),(),(),(1

1 jkpkiPjiP n

N

kn

),()()(1

jkpkjN

k

n

nPP qππ

lim

1)(1

kN

k

SjjjtXP

SjjjXP

),())((

),())0((

Discrete time Markov chain: summary

• stochastic process X(t) countable or finite state space S

Markov property

time homogeneous

independent t

irreducible: each state in S reachable from any other state in S

transition probabilities

Assume ergodic (irreducible, aperiodic) global balance equations (equilibrium eqns)

solution that can be normalised is equilibrium distributionif equilibrium distribution exists, then it is unique and is limiting distribution

))(|)((

))(,...,)(|)((

11

1111

nnnn

nnnn

jtXjtXP

jtXjtXjtXP

))(|)(( jtXktXP

))(|)1((),( jtXktXPkjp

1),(

kjpSk

).()()( jkpkjSk

)())0(|)((lim kjXktXPt

Random walk

http://www.math.uah.edu/stat/

• Gambling game over infinite time horizon: on any turn– Win €1 w.p. p– Lose €1 w.p. 1-p – Continue to play

– Xn= amount after n plays

– State space S = {…,-2,-1,0,1,2,…}

– Time homogeneous Markov chain

– For each finite time n:

– But equilibrium?

piip

iXjXPpiip nn

1)1,(

)|()1,( 1

)|( 0 iXjXP n

http://www.math.uah.edu/stat



http://www.math.uah.edu/stat/

Today:


Continuous time Markov chain• stochastic process X(t)

countable or finite state space S

Markov property

transition probability


Chapman-Kolmogorov equation

transition rates or jump rates

))(|)((

))(...,)(,)(|)(( 11

itXjstXP

jtXjtXitXjstXP nn

))0(|)((),( iXjtXPjiPt

),(),(),( jkPkiPjiP stk

st

jih

jiPjiq h

h

),(lim),(

0

)(),(),( hohjiqjiPh

Continuous time Markov chain

• Chapman-Kolmogorov equation

transition rates or jump rates

• Kolmogorov forward equations: (REGULAR)

Global balance equations

),(),(),( jkPkiPjiP stk

st

jih

jiPjiq h

h

),(lim),(

0

)],()(),()([0

)],(),(),(),([),('

)],(),(),(),([

]1),()[,(),(),(),(),(

),(),(),(

kjqjjkqk

kjqjiPjkqkiPjiP

kjPjiPjkPkiP

jjPjiPjkPkiPjiPjiP

jkPkiPjiP

jk

ttjk

t

hthtjk

hthtjk

tht

htk

ht

Continuous time Markov chain: summary

• stochastic process X(t) countable or finite state space S

Markov property

transition rates

independent t


Assume ergodic and regular global balance equations (equilibrium

eqns)

π is stationary distribution

solution that can be normalised is equilibrium distributionif equilibrium distribution exists, then it is unique and is limiting distribution

)],()(),()([0 kjqjjkqkjk

)())0(|)((lim kjXktXPt

))(|)((

))(...,)(,)(|)(( 11

itXjstXP

jtXjtXitXjstXP nn

jih

jiPjiq h

h

),(lim),(

0

Today:


Next time:

• [R+SN] section 1.1 – 1.3

• Continuous – time Markov chains:

Birth-death processes; Poisson process, simple queue;reversibility; detailed balance;

Today:


Exercises:

• [R+SN] 1.1.2, 1.1.4, 1.1.5

• Give proof of Chapman-Kolmogorov equation

• For random walk, letDetermine the possible states for N=10, and compute for all feasible j

• Consider the random walk with reflecting boundary, that has transition probabilities similar to random walk, except in state 0. When the process attempts to jump to the left in state 0, it stays at 0. The transition probabilities are

Show that a solution of the global balance equations is

For which values of p is this an equilibrium distribution?

1)0( 0 XP

)0|( 010 XjXP

pppp

ipiippiip

1)0,0(,)1,0(

,...3,2,1,1)1,(,)1,(

i

p

pci

1

)(

Flows and Networks (158052)

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