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Flows and Networks Plan for today (lecture 4):

Flows and Networks Plan for today (lecture 4):. Last time / Questions? Output simple queue Tandem network Jackson network: definition Jackson network: equilibrium distribution Partial balance Kelly/Whittle network Examples Summary / Next Exercises.

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Flows and Networks Plan for today (lecture 4):

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  1. Flows and NetworksPlan for today (lecture 4): • Last time / Questions? • Output simple queue • Tandem network • Jackson network: definition • Jackson network: equilibrium distribution • Partial balance • Kelly/Whittle network • Examples • Summary / Next • Exercises

  2. Flows and NetworksPlan for today (lecture 3): • Last time / Questions? • Reversibility, stationarity • Truncation • Kolmogorov’s criteria • Poisson process • PASTA • Output simple queue • Tandem network • Summary / Next • Exercises

  3. Poisson process • Definition : Poisson process :Let S1,S2,… be a sequence of independent exponential() r.v. Let Tn=S1+…+Sn, T0=0 and N(s)=max{n,Tn≤s}. The counting process {N(s),s≥0} is called Poisson process. • Theorem : If {N(s),s≥0} is a Poisson process, then(i) N(0)=0,(ii) N(t+s)-N(s)=Poisson( t), and(iii) N(t) has independent increments.Conversely, if (i), (ii), (iii) hold, then {N(s),s≥0} is a Poisson process

  4. Flows and NetworksPlan for today (lecture 3): • Last time / Questions? • Reversibility, stationarity • Truncation • Kolmogorov’s criteria • Poisson process • PASTA • Output simple queue • Tandem network • Summary / Next • Exercises

  5. fraction of time system in staten probability outside observer seesncustomers at timet probability that arriving customer sees n customers at time t (just before arrival at time t there are n customers in the system) in general PASTA: Poisson Arrivals See Time Averages

  6. Let C(t,t+h) event customer arrives in (t,t+h) For Poisson arrivals q(n,n+1)= so that Alternative explanation; PASTA holds in general! PASTA: Poisson Arrivals See Time Averages PASTA

  7. Flows and NetworksPlan for today (lecture 3): • Last time / Questions? • Reversibility, stationarity • Truncation • Kolmogorov’s criteria • Poisson process • PASTA • Output simple queue • Tandem network • Summary / Next • Exercises

  8. Simple queue, Poisson() arrivals, exponential() service X(t) number of customers in M/M/1 queue: in equilibrium reversible Markov process. Forward process: upward jumps Poisson () Reversed process X(-t): upward jumps Poisson () = downward jump of forward process Downward jump process of X(t) Poisson () process Output simple queue

  9. Let t0 fixed. Arrival process Poisson, thus arrival process after t0 independent of number in queue at t0. For reversed process X(-t): arrival process after –t0 independent of number in queue at –t0 Reversibility: joint distribution departure process up to t0 and number in queue at t0 for X(t) have same distribution as arrival process to X(-t) up to –t0 and number in queue at –t0. In equilibrium the departure process from an M/M/1 queue is a Poisson process, and the number in the queue at time t0 is independent of the departure process prior to t0 Holds for each reversible Markov process with Poisson arrivals as long as an arrival causes the process to change state Output simple queue (2)

  10. Flows and NetworksPlan for today (lecture 3): • Last time / Questions? • Reversibility, stationarity • Truncation • Kolmogorov’s criteria • Poisson process • PASTA • Output simple queue • Tandem network • Summary / Next • Exercises

  11. Simple queue, Poisson() arrivals, exponential() service Equilibrium distribution Tandem of J M/M/1 queues, exp(i) service queue i Xi(t) number in queue i at time t Queue 1 in isolation: simple queue. Departure process queue 1 Poisson, thus queue 2 in isolation: simple queue State X1(t0) independent departure process prior to t0,but this determines (X2(t0),…, XJ(t0)), hence X1(t0) independent (X2(t0),…, XJ(t0)). Similar Xj(t0) independent (Xj+1(t0),…, XJ(t0)). Thus X1(t0), X2(t0),…, XJ(t0) mutually independent, and Tandem network of simple queues

  12. Flows and NetworksPlan for today (lecture 5): • Last time / Questions? • Waiting time simple queue • Little • Sojourn time tandem network • Jackson network: mean sojourn time • Summary / Next • Exercises

  13. Waiting time simple queue (1) • Consider simple queue FCFS discipline • W : waiting time typical customer in M/M/1(excludes service time) • N customers present upon arrival • Sr (residual) service time of customers present PASTA Voor j=0,1,2,…

  14. Waiting time simple queue (2) • Thus • is exponential (-)

  15. Flows and NetworksPlan for today (lecture 5): • Last time / Questions? • Waiting time simple queue • Little • Sojourn time tandem network • Jackson network: mean sojourn time • Summary / Next • Exercises

  16. Little’s law (1) • Let • A(t) : number of arrivals entering in (0,t] • D(t) : number of departure from system (0,t] • X(t) : number of jobs in system at time t Equilibrium for t∞ In equilibrium: average number of arrivals per time unit = average number of departures per time unit

  17. Little’s law (2) Fj sojourn time j-th departing job S(t) obtained sojourn times jobs in system at t

  18. Little’s law (3) Assume following limits exist(ergodic theory, see SMOR) Then Little’s law

  19. Little’s law (4) • Intuition • Suppose each job pays 1 euro per time unit in system • Count at arrival epoch of jobs: job pays at arrival for entire duration in system, i.e., pays EF • Total average amount paid per time unit  EF • Count as cumulative over time: system receives on average per time unit amount equal to average number in system • Amount received per time unit EX • Little’s law valid for general systems irrespective of order of service, service time distribution, arrival process, …

  20. Flows and NetworksPlan for today (lecture 5): • Last time / Questions? • Waiting time simple queue • Little • Sojourn time tandem network • Jackson network: mean sojourn time • Summary / Next • Exercises

  21. Sojourn time tandem simple queues Recall: In equilibrium the departure process from an M/M/1 queue is a Poisson process, and the number in the queue at time t0 is independent of the departure process prior to t0 Theorem 2.2: If service discipline at each queue in tandem of J simple queues is FCFS, then in equilibrium the waiting times of a customer at each of the J queues are independent Proof: Kelly p. 38 Tandem M/M/s queues: overtaking Distribution sojourn time: Ex 2.2.2

  22. Flows and NetworksPlan for today (lecture 4): • Last time / Questions? • Output simple queue • Tandem network • Jackson network: definition • Jackson network: equilibrium distribution • Partial balance • Kelly/Whittle network • Summary / Next • Exercises

  23. Simple queues, exponential service queue j, j=1,…,J statemovedepartarrive Transition rates Traffic equations Irreducible, unique solution, interpretation, exercise Jackson network: open Gordon Newell network: closed Jackson network : Definition

  24. Flows and NetworksPlan for today (lecture 4): • Last time / Questions? • Output simple queue • Tandem network • Jackson network: definition • Jackson network: equilibrium distribution • Partial balance • Kelly/Whittle network • Summary / Next • Exercises

  25. Simple queues, Transition rates Traffic equations Closed network Open network Global balance equations: Closed network: Open network: Jackson network : Equilibrium distribution

  26. Transition rates Traffic equations Closed network Global balance equations: Theorem: The equilibrium distribution for the closed Jackson network containing N jobs is Proof closed network : equilibrium distribution

  27. Flows and NetworksPlan for today (lecture 4): • Last time / Questions? • Output simple queue • Tandem network • Jackson network: definition • Jackson network: equilibrium distribution • Partial balance • Kelly/Whittle network • Summary / Next • Exercises

  28. Global balance verified via partial balanceTheorem: If distribution satisfies partial balance, then it is the equilibrium distribution. Interpretation partial balance Partial balance

  29. Transition rates Traffic equations Open network Global balance equations: Theorem: The equilibrium distribution for the open Jackson network containing N jobs is, provided αj<1, j=1,…,J, Proof Jackson network : Equilibrium distribution

  30. Flows and NetworksPlan for today (lecture 4): • Last time / Questions? • Output simple queue • Tandem network • Jackson network: definition • Jackson network: equilibrium distribution • Partial balance • Kelly/Whittle network • Summary / Next • Exercises

  31. Transition ratesfor some functions:S[0,), :S(0,) Traffic equations Open network Partial balance equations: Theorem: Assume that then satisfies partial balance, and is equilibrium distribution Kelly / Whittle network Kelly / Whittle network

  32. Independent service, Poisson arrivals Alternative Examples

  33. Simple queue s-server queue Infinite server queue Each station may have different service type Examples

  34. Flows and NetworksPlan for today (lecture 4): • Last time / Questions? • Output simple queue • Tandem network • Jackson network: definition • Jackson network: equilibrium distribution • Partial balance • Kelly/Whittle network • Summary / Next • Exercises

  35. Summary / next: Equilibrium distributions • Reversibility • Output reversible Markov process • Tandem network • Jackson network • Partial balance • Kelly-Whittle network NEXT: Sojourn times

  36. Exercises [R+SN] 2.1.1, 2.1.2, 2.3.1, 2.3.4, 2.3.5, 2.3.6, 2.4.1, 2.4.2, 2.4.6, 2.4.7

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