Flows and Networks (158052) Richard Boucherie Stochastische 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 Size 5 ECTS 32 hours of lectures : 16 R.J. Boucherie focusing on stochastic networks 16 W. Kern focusing on deterministic networks Common problem How 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
Introduction to theorie of flows in complex networks: both stochastic and deterministic apects Size 5 ECTS 32 hours of lectures : 16 R.J. Boucherie focusing on stochastic networks 16 W. Kern focusing on deterministic networks Common problem - PowerPoint PPT Presentation
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Flows and Networks (158052)
Richard BoucherieStochastische Operations Research -- TW
How to allocate servers / capacity to nodes orhow to route jobs through the systemto maximize system performance, such as throughput, sojourn time, utilization
Reversibility; stationarity• Stationary process: A stochastic process is
stationary if for all t1,…,tn,
• Theorem: If the initial distribution is a stationary distribution, then the process is stationary
• Reversible process: A stochastic process is reversible if for all t1,…,tn,
NOTE: labelling of states only gives suggestion of one dimensional state space; this is not required
))(),...,(),((~))(),...,(),(( 2121 nn tXtXtXtXtXtX
1)(
jSj
))(),...,(),((~))(),...,(),(( 2121 nn tXtXtXtXtXtX
Reversibility; stationarity• Lemma: A reversible process is stationary.
• Theorem: A stationary Markov chain is reversible if and only if there exists a collection of positive numbers π(j), jS, summing to unity that satisfy the detailed balance equations
When there exists such a collection π(j), jS, it is the equilibrium distribution
• Proof
Skjjkqkkjqj ,),,()(),()(
Lemma 1.9 / Corollary 1.10:
If the transition rates of a reversible Markov process with
state space S and equilibrium distribution are
altered by changing q(j,k) to cq(j,k) for
where c>0 then the resulting Markov process is
reversible in equilibrium and has equilibrium distribution
where B is the normalizing constant.
If c=0 then the reversible Markov process
is truncated to A and the resulting Markov
process is reversible with equilibrium distribution
Truncation of reversible processes
Sjj ),(
10
ASkAj \,
ASjjBcAjjB\)(
)(
Ajkj
Ak
)()(
A
S\A
Time reversed processX(t) reversible Markov process X(-t) also, butLemma 1.11: tijdshomogeneity not inherited for
non-stationary process
Theorem 1.12 : If X(t) is a stationary Markov process with transition rates q(j,k), and equilibrium distribution π(j), jS, then the reversed processX(-t) is a stationary Markov process with transition rates
and the same equilibrium distribution
Theorem 1.13: Kelly’s lemmaLet X(t) be a stationary Markov processwith transition rates q(j,k). If we can find a collection of numbers q’(j,k) such that q’(j)=q(j), jS, and a collection of positive numbers (j), jS, summing to unity, such that
then q’(j,k) are the transition rates of the time-reversed process, and (j), jS, is the equilibrium distribution of both processes.
)(),()(),('
jjkqkkjq
Skj ,
),(')(),()( jkqkkjqj Skj ,
Kolmogorov’s criteria• Theorem 1.8:
A stationary Markov chain is reversible iff
for each finite sequence of states
Notice that
),(),()...,(),(),(),()...,(),(
122311
113221
jjqjjqjjqjjqjjqjjqjjqjjq
nnn
nnn
)0,(),(),()...,(),(),(),()...,(),(),0()0()(
112231
132211
jqjjqjjqjjqjjqjjqjjqjjqjjqjqj
nnn
nnn
Sjjj n ,...,, 21
Today:
• Introduction / motivation course• Discrete-time Markov chain• Continuous time Markov chain• Birth-death process• Example: pure birth process• Example: pure death process• Simple queue• General birth-death process: