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The Art of Computer Systems Performance Analysis: Techniques for Experimental Design, Measurement, Simulation and Modeling Paperback – 2008
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About the Author
Raj Jain is a Senior Consulting Engineer in the Distributed Systems Architecture and Performance Group at Digital Equipment Corporation. With over sixteen years of experience in the field of computer systems performance, he is currently responsible for analyzing various design alternatives for DEC's networking architecture. He received the Ph.D. degree from Harvard and has taught courses on performance at Massachusetts Institute of Technology. "Dr. Jain is a Fellow of the IEEE and is listed in Who's Who in the Computer Industry, 1989."
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Writing style is clear and suitable for self-study as well.
The copy delivered to me is a 2012 reprint; hence looks a little bit older.
In my library, I have seen a 2014 reprint which has a better print quality.
That's why deducting 1 star :D
Most helpful customer reviews on Amazon.com
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".
Part II, "Measurement Techniques and Tools", are where things get interesting. The good part about this entire book is that it uses problems in the analysis of computer systems as the basis of presentation for all tools presented. The graphs are excellent, the mathematics are largely self-contained, and if algorithms are presented they are usually given in numbered steps and an actual computer program shown. This is one drawback of the book - it uses the ancient Simula language for its demonstration code. However, if you are familiar with C, Java, or any of the other mainstream procedural languages, you'll find that Simula looks like very readable pseudocode, so this should not be an obstacle to understanding.
Part III is a section dedicated entirely to probability theory and statistics. Starting with the simple definition of the mean, this handy section not only derives all of the statistics you need in this book, it talks about common mistakes made in applying them.
Part IV is about experimental design and analysis. Using the mathematics developed in part three this section talks about all aspects of designing a proper experiment for the measurement or simulation of a computer system, including common mistakes and the best choice for the size of your experiment.
Part V presents the key issues in simulation modeling. First it discusses simulation terminology, simulation design criteria, and stopping conditions. Random number generation is the subject of three chapters in reference to inputs to your simulation. Finally there is a chapter on the commonly used distributions such as Bernoulli, beta, binomial, etc. that talks specifically about random number generation algorithms for each of the distributions presented. What makes this section so valuable is that although you may have possibly seen the math before, more than likely you don't know the value of each kind of distribution. This section makes that issue clear in terms of modeling computer performance.
Part VI is on queuing models, and is probably the most difficult section in the book. Although it is one of the better written pieces I have read on queueing theory, it is not as easily grasped as previous sections based on reading the textbook alone. There are examples present, and the book does a good job of presenting "the big picture" as to the use of queueing theory in computer performance analysis, but you may need outside material to really grasp how to set up a queueing problem from a mathematical standpoint.
No other book I've found does such a good job of discussing all of the topics covered and clearly tying it into practical issues in measuring and monitoring system performance. I highly recommend it.
There are far too many errors in the book, and that is quite frustrating. There is plenty of errata, but a new edition of the book should be made with corrections. The book is rather dated, and I wish that in the new edition, it would be placed in the most modern context possible. I would like a companion site to go with the book where readers could discuss difficult material.