Performance Modeling of Operating Systems Using by José M. Garrido

By José M. Garrido

This e-book introduces the elemental strategies and functional simulation te- niques for modeling diverse elements of working structures to check their g- eral habit and their functionality. The methods utilized are obje- orientated modeling and strategy interplay method of discrete-event simu- tion. The ebook relies on the fundamental modeling techniques and is extra really expert than my prior e-book: sensible strategy Simulation with Object-Oriented options and C++, released through Artech residence, Boston 1999. For a extra specified description see the net position: http://science.kennesaw.edu/~jgarrido/mybook,html. so much different books on functionality modeling use merely analytical ways, and extremely few observe those thoughts to the examine of working platforms. therefore, the original function of the ebook is that it concentrates on layout facets of working platforms utilizing sensible simulation ideas. moreover, the e-book illustrates the dynamic habit of alternative features of working structures utilizing many of the simulation types, with a common hands-on approach.

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Extra resources for Performance Modeling of Operating Systems Using Object-Oriented Simulation: A Practical Introduction

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The memory capacity may also be used at capacity (100%). Thus, the bottleneck can be localized at the processor, the queue, or the memory. In any of the three cases, the processor, the queue, or the memory will need to be replaced or increased in capacity. The performance metrics computed in the single-server model, depend on the workload submitted, and on the system parameters. Modifying the workload on a model and/or the parameters changes the behavior of the model; this can be shown by the variations in the value of the performance metrics computed.

The interval [l,u] is called the confidence interval, and the value 1 – q is called the confidence coefficient. The method consists in finding the bounds for the interval, and finding the probability that the population mean (µ) is within that interval. To obtain improved accuracy of the estimates of performance measures, variance reduction methods are used. 18 MARKOV MODELS Systems that exhibit dependencies in their state changes (transitions) are modeled as Markov models. In these systems, the next state depends on the sequence of previous states.

Running, the process is receiving service from the CPU; Wait, the process is waiting for service from an I/O device; Suspended, the process is waiting for some event to occur; I/O, the process is receiving service from an I/O device; Terminated, the process has completed execution. 1. The stochastic matrix for this model is: In the rest of this book, the simple properties of Markov chains will be used in simulation, and the assumption will be taken that these models have a well-defined steady-state behavior.

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