Module6-ECE541

# Module6-ECE541 - Simulation Basics ECE/CS 541 Computer...

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Module 8, Slide 1 ECE/CS 541: Computer System Analysis. ©2005 William H. Sanders. All rights reserved. Do not copy or distribute to others without the permission of the author. Simulation Basics

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Module 8, Slide 2 ECE/CS 541: Computer System Analysis. ©2005 William H. Sanders. All rights reserved. Do not copy or distribute to others without the permission of the author. Motivation High-level formalisms (like SANs) make it easy to specify realistic systems, but they also make it easy to specify systems that have unreasonably large state spaces. State-of-the-art tools (like Mobius) can handle state-level models with a few ten’s of million states, but not more. When state spaces become too large, discrete event simulation is often a viable alternative. Discrete-event simulation can be used to solve models with arbitrarily large state spaces, as long as the desired measure is not based on a “rare event.” When “rare events” are present, variance reduction techniques can sometimes be used.
Module 8, Slide 3 ECE/CS 541: Computer System Analysis. ©2005 William H. Sanders. All rights reserved. Do not copy or distribute to others without the permission of the author. Simulation as Model Experimentation State-based methods (such as Markov chains) work by enumerating all possible states a system can be in, and then invoking a numerical solution method on the generated state space. Simulation, on the other hand, generates one or more trajectories (possible behaviors from the high-level model), and collects statistics from these trajectories to estimate the desired performance/dependability measures. Just how this trajectory is generated depends on the: – nature of the notion of state (continuous or discrete) – type of stochastic process (e.g., ergodic, reducible) – nature of the measure desired (transient or steady-state) – types of delay distributions considered (exponential or general) We will consider each of these issues in this module, as well as the simulation of systems with rare events.

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Module 8, Slide 4 ECE/CS 541: Computer System Analysis. ©2005 William H. Sanders. All rights reserved. Do not copy or distribute to others without the permission of the author. Types of Simulation Continuous-state simulation is applicable to systems where the notion of state is continuous and typically involves solving (numerically) systems of differential equations. Circuit-level simulators are an example of continuous-state simulation. Discrete-event simulation is applicable to systems in which the state of the system changes at discrete instants of time, with a finite number of changes occurring in any finite interval of time. Since we will focus on validating end-to-end systems, rather than circuits, we will
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Module6-ECE541 - Simulation Basics ECE/CS 541 Computer...

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