Performance, Reliability, and Availability Evaluation of Computational Systems, Volume I Performance and Background

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.

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