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Based on Law & Kelton, Simulation Modeling & Analysis, McGraw-Hill

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1 Based on Law & Kelton, Simulation Modeling & Analysis, McGraw-Hill

2 Why Simulation? Test design when cannot analyze Verify analysis
System too complex Can analyze only for certain cases (Poisson arrivals, very large N, etc.) Verify analysis Fast production of results

3 Simulation types Static vs. dynamic Deterministic vs. stochastic
Continuous vs. discrete Simulation model  system model

4 Discrete Event System Discrete system Example: a queueing system
The system state can change only in a countable number of points in time! Event = an instantaneous change in state. Example: a queueing system System state: Number of customer At each time number of customer can only change by an integer

5 Continuous Simulation
Simulating the flight of a rocket in the air System state: rocket position and weight State changes continuously in time (according to a partial differential equation)

6 Components of a discrete-event simulation model
System state: the collection of state variables necessary to describe the system at a particular time. Simulation clock: a variable giving the current value of simulated time Event list: a list containing the next time when each type of event will occur. Statistical counters: variables used for storing statistical information about system performance How many moments?

7 Components of a discrete-event simulation model (2)
Initialization routine: a subprogram to initialize the simulation model at time 0. Timing routine: a subprogram that determines the next event from the event list and then advances the simulation clock to the time when the event is to occur. Event routine: A subprogram that updates the system state when a particular type of event occurs (one routine per event).

8 Components of a discrete-event simulation model (3)
Library routine: A set of subprograms used to generate random observations from probability distributions that were determined as part of the simulation model. Report generator: A subprogram that computes estimates (from the statistical counter) of the desired performance measures when simulation ends.

9 Components of a discrete-event simulation model (4)
Main program: a subprogram that invokes the timing routine to determine the next event and then transfers control to the corresponding event routine to update the system state. May also check for termination and invoke the report generator.

10 Flow control Init Determine next event Event 1 Event n Done? no report

11 Example a single server queuing system
System to be simulated Ai Interarrival times are I.I.D. Si Service time are I.I.D. FIFO service Work preserving Initial state Empty and idle First arrival after Ai time units from time 0 Termination after n customers left queue.

12 Performance measures d(n) = expected average delay in queue.
Customer point of view q(n) = expected time-average number of customers in queue. System point of view Note: average over time which is continuous!! u(n) = proportion of time server is busy

13 No. of customers in queue.
Q(t) 4 3 2 1 t

14 No. of customers in queue.
Q(t) 4 3 2 1 t

15 Performance measures (2)
Averages are not always enough. Register max/min values. Register the entire “pdf” histograms

16 Initialization Set simulation time to 0. Set initial state:
Server idle Queue empty Last event time Init event list Generate 1st arrival Zero stat counters Total delay and number delayed Area under Q(t) and B(t).

17 1st Event: customer arrival
After init finished the arrival event is selected and time is advanced to this event Change server from idle to busy Add 0 to total delay, increment No of delayed. Generate two events: This customer departure Next arrival (generate as you go) Add 0 to the area under Q(t). Add 0 to the area under B(t).

18 2nd Event: customer arrival
Arrival event is selected and time is advanced to this event Server busy => put customer in queue with arrival time Set “No. in queue” to 1. Generate next-arrival events (don’t mess with dep.) Add 0 to the area under Q(t). Add (t2-t1) to the area under B(t).

19 Remarks While handling an event time is sanding
But, order of operation is still important: First update area under Q(t) then update “No. in queue” Two events at the same time Order may change simulation result!

20 Determining Events In complex systems events sequence may not be trivial. Use of event graph to aim us is designing the events

21 Event Graph States are bulbs, connected with arrows
Bold arrow: event may occur. Thin arrow: event must occur. depar- ture arrival

22 Alternative Event Graph
Enter service depar- ture arrival This design is correct as well! One more event => more complexity Simplification rule: If an event node has incoming arcs that are all thin and smooth, it can be eliminated. A strongly connected connected component that have no incoming arcs from other nodes must be initialized.

23 Take care GIGO = garbage in garbage out Statistical confidence
Realistic scenarios (arrival process, service time) Full cover of system behavior Statistical confidence What to model Not enough details => hurts accuracy Too many details => slows simulation Attempt to verify correctness Simulate cases you can analyze

24 Random Generator Make sure you can regenerate your random sequence, or debugging is hell. For long simulations, use 32 bit pseudo random generator. 16 bit is too short!

25 Statistical Confidence
Better a few short runs than one long one. However, make sure run time is long enough to make end conditions negligible. Given a set of IID random variables we can calculate the confidence interval.

26 Confidence Interval X1, X2, …Xn are IID random variable with finite mean  and finite variance 2>0. Fn(z)=P(Zn z) is Dist. Func. of Zn By central limit theorem Fn(z)(z) (the std. Normal r.v.) For sufficiently large n, approx. 100(1-) percent confidence interval for  is given by

27 1 0.9 0.8 0.7 0.6 0.5 Area: 1- 0.4 0.3 0.2 0.1 -5 -3 3 5

28 Confidence Interval for Small n
For small number of observations, we need to correct the coefficient of the interval width The small n the larger tn-1,1-/2 is. tn-1,1-/2 is taken from a table.

29 Confidence Interval In general all plots has to have a confidence interval on every mark Exception: high confidence (mark size)

30 A good pseudo random generator
float myrand() { b32 = ( *b ); if (b32<0) b32 = -b32; return( * b32); }

31 Generating Non-Uniform Distributions
F(x), the PDF, of any distribution is a function to [0,1], => use the inverse transform Generate U~U(0,1) Return X=F-1(U) P(Xx) = P(F-1(U)  x)=P(U F(x))=F(x) Since U is U(0,1) and 0 F(x) 1, P(U y) = y

32 Generating the Exponential distribution
Note we can use - ln(u).

33 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 20 40 60 80 100 120 140 160 180 200

34 Simulating complex systems
Systems often have many identical components A queueing system with multiple queues A switch with multiple ports running a distributed algorithm Must keep state for each component Array of structures in C, multiple instances of the basic object (C++) Keep the connectivity (when applicable)

35 Exercise No. 1 Write an event simulator for a single queue Hand ins:
M/M/1 M/D/1 Hand ins: Code transcript Utilization vs. load plot with confidence interval of 90%, and the theoretical expectation With 5 executions, and with 20 executions Average delay (queueing +service) vs. load

36 Remarks Code has to work anywhere (not just at home), especially in TAU. Make it portable. Oral exams through the semester. Send code to Specify development env. (Unix/Visual C/..) One tar/zip file with makefile etc.

37 Exercise No.2 Give an exact expression (neglect history) to the bound of the throughput of an NxN switch given that the scheduler can select any of the two packets at the HOL. What happens when N. Plot the bound as a function of the depth of the queue the scheduler can look at.

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