This old book is one of the most valued on my shelf. I was first exposed to it in a graduate class, and I have to say that the book is so good I was not aware that my professor was not a good instructor until I had him a second time in a class where the textbook was less than stellar. Don't judge the book by a quick perusal either. At first glance, especially if you are looking at Part I, it looks like one of those books on Six Sigma that will put you to sleep. In fact, the vast majority of the book is quite interesting.
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.
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