Customer Reviews

The Art of Computer Systems Performance Analysis: Techniques for Experimental Design, Measurement, Simulation, and Modeling

5 star:		(14)
4 star:		(2)
3 star:		(1)
2 star:		(0)
1 star:		(1)

 

 

This seminal book was the most complete mainstream text on the subject when it was first published in 1991. The reason it remains popular eleven years later (which is an extraordinary life for a computer book) is because it's an applied mathematics book that focuses solely on performance analysis, simulation and measurements. Technology may change at a rapid pace, but the techniques for measuring it do not.

The book is broken into six parts consisting of 36 chapters and ten appendices. Part I is an overview of performance evaluation. I liked the chapter on common mistakes and how to avoid them, and the guidelines for selecting techniques and metrics.

Measurement techniques and tools are the focus of Part II. the eight chapters in this part address the full spectrum, including workload types, characterization techniques, capacity planning and benchmarking, and data presentation. Most of the chapters are generic enough to be timeless. The chapters covering execution monitors and accounting logs are showing their age, but the concepts are still valid. I especially liked the chapters about data presentation and ratios.

Part III is a refresher in statistics and probability, and can safely be skipped if your knowledge and skills are fresh. Part IV delves into experimental design and analysis, while Part V covers simulations. The remaining six chapters of the book, Part VI, are devoted to queuing models.

Note that although the math is clearly explained you need college level skills in order to fully comprehend the techniques presented. I recommend investing in MathCAD or a similar program to make it easy to work the exercises that end each chapter.

This book is one of the handful that will be on your bookshelf for years to come. It's probably one of the most frequently referenced works in the performance analysis, resource management and capacity planning knowledge domains. Because it has well designed exercises at the end of each chapter it's also well suited as an advanced college level text. In my opinion it's essential reading for performance and capacity analysts, and provides an excellent foundation for more specialized books, such as those by Menasce and Almeida, that address topics such as e-commerce performance, web services, etc.

This book is the most complete performance analysis book I have ever seen. The theory is excellent and the examples are very practical. It covers details such as Markov chains and the corresponding equations. Truly, it's one of the books that I've enjoyed reading the most. Jain has produced a masterpiece

I found this book by accident looking for some statistics help for a benchmarking project. It turned out to be worth its weight in gold as it is very clear, comphrehensive, and readable.

He outlines and elaborates a methodical, straightforward approach to performance analysis and provides excellent sections on what to do, not do, and how to validate or refute the analyses of others.

Extensive examples with solutions and enough equations to let you easily implement the analysis portions in code (or a spreadsheet if need be).

If you are doing any kind of performance analysis, whether it is for computer systems or not, you should get a copy.

The only reason I didnt give it 5 stars is a very small number of the example answers have typos or rounding errors. Be sure to get the errata list from the website.

This is an absolute gem. I really think that this book must be read by every CS/EE engineer. Performance analysis is always something that comes as an afterthought and engineers think that some some "mathematical" guy called a perf analyst will stop by after the product is completed and analyze the product. This is def wrong. PA should be done at every stage of production.

This book has been written assuming a novice reader. Several parts of the book have to re-read to really understand what the author is trying to convey, but trust me you will really appreciate it.

I suggest reading the following parts of the book(in order):

Part I (whole)
Part II (4, 5, 6, browse {7,8}, 9, 10 ) 10 is cool stuff..impress your peers with this
Part III (whole) read, re-read, re-read till you have digested every line..worth it really!!!!
Part IV (whole) read, re-read, re-read till you have digested every line..worth it really!!!!
Part V : You can skip this if you are not into simulation.
Part VI : Not really that easy to follow. I suggest Gunter's book for this.

This book is the most impressive performance assessment, modeling and simulation text I have read. It is a perfect balance of systems analysis and the necessary mathematical foundations that are extremely important to fully understand the disciplines Jain covers in this text. I have used this book numerous times for capacity planning projects, simulation studies, statistical analysis and for a guide in delivering presentations and writing papers.

This book is a classic in its field. It covers performance analysis for computers, but is very easily extended to any area of performance analysis. It covers some theory, but mainly tips and "stories around the campfire" from those with a great deal of experience in the area. And that's actually more valuable than pure academic theory. The math is not too bad. One great feature is that you can focus on only those areas that you are concerned about and you can go as in-depth as you want. It's also true that it will never become obsolete (although some examples might be).

While reading this book, I went back to my university years, and I realize mathematics can help a wide of real life situations.

As the author states, one of the common mistakes of recently graduated students is to think a real life easy problem with a complex mathematical model; I think one of the major problems with the IT mature professionals today, is that they try to solve complex problems following somekind of a deductive method; usually we override any kind of analysis because we think mathematical models or anything related is for university people....

While this book is very practical, it's also very impressive with it's mathematical rigour. For any performance consultant (database, server, network, etc) this book is very valuable guide for developing capacity planning and performance projects.

Don't expect to find very updated examples, but surely you won't need them if you understand the idea behind the concepts.

If you want to learn about Capacity Management and you don't have the time to follow a course during day time this book will give you the training you were looking for. It has a lot of useful information and examples about the subject. It will give you all the basic information you need in this area, shows you the angles and gives you the right handles for this complex area of IT.

Excellent book with all the math foundations for understanding and designing statistical models for systems performance analysis.

This book will never become obsolete and if you liked Donald Knuth Computer Programming Series this one is for you.

This is a reference book and it requires some math skills and background to understand.

I had to buy this book as main recommended reference for one of the compulsory subjects of my graduate studies. My focus and interests areas are more into Computational Science and Machine Learning so I honestly wasn't that eager to study it. The coverage on performance analysis is excellent. I learned a lot from it e.g. Queuing Theory. I recommend it for this purpose, however this is not the focus of my review.

I own most of the best books in the areas of Machine Learning and Statistics. It really amaze me how this book alone with a very uncluttered and pragmatic way clearly explains and support with detailed step by step examples what most of the other best books in those areas miserably fail to show. This book offers one of the best introductions to Statistics I know of e.g. explanation of t-test, chi-square test, confidence intervals, ANOVA etc. There is really no better book I know for explaining what PCA is all about ... all the Machine Learning books I own spend many pages even chapters and fail to clearly show the concept this book do show in just a couple of pages ... really impressive!

Don't be fooled by the publication date, the concepts are still very relevant and there is no book on Statistics I can recommend better than this one. Plus you will learn statistics with excellent performance analysis examples. This is the perfect mix to have e.g. software developer taking a refresher in Statistics. However, do pay attention to the *reprint* date as there are multiple prints around and the errata is quite large e.g. avoid buying an old print from the "Marketplace".