CPSC 441 Tutorial TA: Fang Wang. Simulation Methodology Plan: Introduce basics of simulation modeling Define terminology and methods used Introduce simulation.

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Presentation transcript:

CPSC 441 Tutorial TA: Fang Wang

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

Simulations Widely used in computing and technology for helping to understand the behavior of systems that are too hard to model in other forms Processor scheduling Traffic and highway analysis Rivers and streams and flooding Robot movement and control Network congestion Satellites and space craft … From: web.cs.wpi.edu/.../Assignment3--EventDrivenSimulation.ppt

Performance Evaluation Analytical Methods Simulation Methods Experimental Methods

Performance Evaluation Analytical Methods Simulation Methods Experimental Methods Time-DrivenEvent-DrivenMonte Carlo...

Performance Evaluation Analytical Methods Simulation Methods Experimental Methods Time-DrivenEvent-DrivenMonte Carlo Sequential ParallelDistributed...

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

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)

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

Sequential Simulation Assumes a single processor system Uses central event list (ordered by time) Global state information available Single, well-defined notion of time Many clever implementation techniques and data structures for optimizing event list management Linked list; doubly-linked list; priority queue; heap; calendar queue; trie structure

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

Random Number Generators Random numbers are needed frequently in engineering & scientific computations Simulations, arrival times, etc. Exercising other code Analyzing system performance Definition: Random Number Generator A function that returns a seemingly random number each time it is called (Usually) within a specified range From: web.cs.wpi.edu/.../Assignment3--EventDrivenSimulation.ppt

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

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