Probability, Stochastic Processes, and Queueing Theory: The Mathematics of Computer Performance Modeling Review

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(More customer reviews)This book is suitable for my graduate studies on computer performance. The author directs us from combinatorics, distribution theory, queue theory to queueing networks in a systematic way. I have read the book at ease for its stepwise elaboration of concepts. However, I have also read with hardship as it requires the readers to possess a good command of mathematics, both pure and applied, in order to go through the book.
For a mathematics graduate studying computer networks, I recommend this book. A novice or a mediocrity should pay more patience to read if not yet at a loss.
This book has aroused my interest and eagerness to know more about computer performance from the viewpoint of queueing and networking. In a word, I enjoy reading this book.

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This textbook provides a comprehensive introduction to probability and stochastic processes, and shows how these subjects may be applied in computer performance modelling. The author's aim is to derive the theory in a way that combines its formal, intuitive, and applied aspects so that students may apply this indispensable tool in a variety of different settings. Readers are assumed to be familiar with elementary linear algebra and calculus, including the concept of limit, but otherwise this book provides a self-contained approach suitable for graduate or advanced undergraduate students. The first half of the book covers the basic concepts of probability including expectation, random variables, and fundamental theorems. In the second half of the book the reader is introduced to stochastic processes. Subjects covered include renewal processes, queueing theory, Markov processes, and reversibility as it applies to networks of queues. Examples and applications are drawn from problems in computer performance modelling.

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Modeling, Analysis, Design, and Control of Stochastic Systems (Springer Texts in Statistics) Review

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(More customer reviews)This is probably ideal as a reference source for a graduate student or professor who knows stochastics very well already.
However, if you are a novice trying to learn about stochastics and want good explanations and examples with an appropriate buildup, I would not recommend the book.
As an example, the review discussion of probability in the first four chapters didn't even come close to comparing with the probability book I used in another class. If you are near a bookstore, you can easily verify this. I imagine that this comparison (or lack thereof) would hold for many other probability textbooks. Also, if presentation makes a difference to you, this is quite minimalist.
Another area that I found lacking is that the answers in the back just provide a numerical answer without any explanation to how solutions were arrived at. While this is often the case for other books, the author did not provide a sufficient base for a novice to work the problems. As a result, most of the end of chapter problems were of little use in helping me better learn the materials. A good workbook or better explanations would be very helpful.
While there are certainly couple areas that I found worthwhile and this does appear to be one of the only books on this niche area (the lack of competition may explain a lot of why the shortcomings exist and why this doesn't have the feel of real textbook), this first edition book needs some serious work to make it truly effective and user friendly.

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An introductory level text on stochastic modelling, suited for undergraduates or graduates in actuarial science, business management, computer science, engineering, operations research, public policy, statistics, and mathematics. It employs a large number of examples to show how to build stochastic models of physical systems, analyse these models to predict their performance, and use the analysis to design and control them. The book provides a self-contained review of the relevant topics in probability theory: In discrete and continuous time Markov models it covers the transient and long term behaviour, cost models, and first passage times; under generalised Markov models, it covers renewal processes, cumulative processes and semi-Markov processes. All the material is illustrated with many examples, and the book emphasises numerical answers to the problems. A software package called MAXIM, which runs on MATLAB, is available for downloading.

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Markov Processes for Stochastic Modeling (Stochastic Modeling Series) Review

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(More customer reviews)It is the worst book about stochastic processes I've ever read, it's confusing, if there were concepts clear in your mind after reading the book there won't be anymore. It is a very useful book to burn.

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