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Full Description
This textbook intends to be a comprehensive and substantially self-contained two-volume book covering performance, reliability, and availability evaluation subjects. The volumes focus on computing systems, although the methods may also be applied to other systems. The first volume covers Chapter 1 to Chapter 14, whose subtitle is ``Performance Modeling and Background". The second volume encompasses Chapter 15 to Chapter 25 and has the subtitle ``Reliability and Availability Modeling, Measuring and Workload, and Lifetime Data Analysis".
This text is helpful for computer performance professionals for supporting planning, design, configuring, and tuning the performance, reliability, and availability of computing systems. Such professionals may use these volumes to get acquainted with specific subjects by looking at the particular chapters. Many examples in the textbook on computing systems will help them understand the concepts covered in each chapter. The text may also be helpful for the instructor who teaches performance, reliability, and availability evaluation subjects. Many possible threads could be configured according to the interest of the audience and the duration of the course. Chapter 1 presents a good number of possible courses programs that could be organized using this text.
Volume I is composed of the first two parts, besides Chapter 1. Part I gives the knowledge required for the subsequent parts of the text. This part includes six chapters. It covers an introduction to probability, descriptive statistics and exploratory data analysis, random variables, moments, covariance, some helpful discrete and continuous random variables, Taylor series, inference methods, distribution fitting, regression, interpolation, data scaling, distance measures, and some clustering methods. Part II presents methods for performance evaluation modeling, such as operational analysis, Discrete-Time Markov Chains (DTMC), and Continuous Time Markov Chains (CTMC), Markovian queues, Stochastic Petri nets (SPN), and discrete event simulation.
Contents
Chapter 1 Introduction
PART I Fundamental Concepts
Chapter 2 Introduction to Probability
Chapter 3 Exploratory Data Analysis
Chapter 4 Introduction to Random Variables
Chapter 5 Some Important Random Variables
Chapter 6 Statistical Inference and Data Fitting
Chapter 7 Data Scaling, Distances and Clustering
PART II Performance Modeling
Chapter 8 Operational Analysis
Chapter 9 Discrete Time Markov Chain
Chapter 10 Continuous Time Markov Chain
Chapter 11 Basic Queueing Models
Chapter 12 Petri Nets
Chapter 13 Stochastic Petri Nets
Chapter 14 Stochastic Simulation
