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Simulation Methodology

Simulation Methodology. Plan: Introduce basics of simulation modeling Define terminology and methods used Introduce simulation paradigms Time-driven simulation Event-driven simulation Monte Carlo simulation Technical issues for simulations Random number generation

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Simulation Methodology

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  1. Simulation Methodology • Plan: • Introduce basics of simulation modeling • Define terminology and methods used • Introduce simulation paradigms • Time-driven simulation • Event-driven simulation • Monte Carlo simulation • Technical issues for simulations • Random number generation • Statistical inference CPSC 641 Winter 2011

  2. Time-Driven Simulation • Time advances in fixed size steps • Time step = smallest unit in model • Check each entity to see if state changes • Well-suited to continuous systems • e.g., river flow, factory floor automation • Granularity issue: • Too small: slow execution for model • Too large: miss important state changes CPSC 641 Winter 2011

  3. Event-Driven Simulation (1 of 2) • Discrete-event simulation (DES) • System is modeled as a set of entities that affect each other via events (msgs) • Each entity can have a set of states • Events happen at specific points in time (continous or discrete), and trigger state changes in the system • Very general technique, well-suited to modeling discrete systems (e.g, queues) CPSC 641 Winter 2011

  4. Event-Driven Simulation (2 of 2) • Typical implementation involves an event list, ordered by time • Process events in (non-decreasing) timestamp order, with seed event at t=0 • Each event can trigger 0 or more events • Zero: “dead end” event • One: “sustaining” event • More than one: “triggering” event • Simulation ends when event list is null, or desired time duration has elapsed CPSC 641 Winter 2011

  5. Monte Carlo Simulation • Estimating an answer to some difficult problem using numerical approximation, based on random numbers • Examples: numerical integration, primality testing, WSN coverage • Suited to stochastic problems in which probabilistic answers are acceptable • Might be one-sided answers (e.g., prime) • Can bound probability to some epsilon CPSC 641 Winter 2011

  6. Summary • Simulation methods offer a range of general-purpose approaches for perf eval • Simulation modeler must determine the appropriate aspects of system to model • “The hardest part about simulation is deciding what not to model.” - M. Lavigne • Many technical issues: RNG, validation, statistical inference, efficiency • We will look at some examples soon CPSC 641 Winter 2011

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