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Performance by Design: Computer Capacity Planning By Example 1st Edition

4.9 out of 5 stars 7 customer reviews
ISBN-13: 007-6092028048
ISBN-10: 0130906735
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From the Back Cover

Practical systems modeling: planning performance, availability, security, and more

Computing systems must meet increasingly strict Quality of Service (QoS) requirements for performance, availability, security, and maintainability. To achieve these goals, designers, analysts, and capacity planners need a far more thorough understanding of QoS issues, and the implications of their decisions. Now, three leading experts present a complete, application-driven framework for understanding and estimating performance. You'll learn exactly how to map real-life systems to accurate performance models, and use those models to make better decisions--both up front and throughout the entire system lifecycle. Coverage includes:

  • State-of-the-art quantitative analysis techniques, supported by extensive numerical examples and exercises
  • QoS issues in requirements analysis, specification, design, development, testing, deployment, operation, and system evolution
  • Specific scenarios, including e-Business and database services, servers, clusters, and data centers
  • Techniques for identifying potential congestion at both software and hardware levels
  • Performance Engineering concepts and tools
  • Detailed solution techniques including exact and approximate MVA and Markov Chains
  • Modeling of software contention, fork-and-join, service rate variability, and priority
About the Web Site

The accompanying Web site provides companion Excel workbooks that implement many of the book's algorithms and numerical examples.

About the Author

DANIEL A. MENASCe is Professor of Computer Science at George Mason Universityand a Fellow of the Association for Computing Machinery. He has consulted extensivelyon Web performance worldwide. Menasce holds a Ph.D. in Computer Science fromUCLA.

VIRGILIO ALMEIDA, Professor of Computer Science at the FederalUniversity of Minas Gerais, Brazil, holds a Ph.D. in Computer Science from VanderbiltUniversity and was formerly a visiting researcher at Xerox PARC and at HP Labs. Menasce and Almeida co-authored "Scaling for E-Business" (Prentice Hall PTR).

Almeida is Professor and Chair of the Computer Science Department at the Federal University of Minas Gerais, Brazil, has served as visiting researcher at Xerox PARC and HP Research Laboratories.

Dowdy is Professor and former Chair of Computer Science at Vanderbilt University.

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Product Details

  • Paperback: 480 pages
  • Publisher: Prentice Hall; 1 edition (January 15, 2004)
  • Language: English
  • ISBN-10: 0130906735
  • ISBN-13: 978-0130906731
  • Product Dimensions: 7.1 x 1 x 9.6 inches
  • Shipping Weight: 1.8 pounds (View shipping rates and policies)
  • Average Customer Review: 4.9 out of 5 stars  See all reviews (7 customer reviews)
  • Amazon Best Sellers Rank: #1,109,371 in Books (See Top 100 in Books)

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Top Customer Reviews

This team of authors has produced yet another invaluable book for practitioners who perform capacity and performance planning, as well as students who are introduced to this topic for the first time. Unlike earlier works by the authors, which addressed performance in specific systems environments such as client/server, e-business and web services, this book is more general. Therein lies the true value - it teaches the fundamentals and will not be soon outdated.
The book is structured into two parts - Part I consists of four chapters that lay the foundation. Chapter 1 covers system life cycles, Chapter 2 moves the reader from systems to descriptive models of the systems, and Chapters 3 and 4 delve into the essence of performance - quantifying performance models and giving a performance engineering methodology. This material is reinforced with five chapters, each of which is a case study of a specific performance problem. These include database services, web servers, data center, e-business services and help-desk services.
Part II, The Theory of Performance Engineering, addresses the underlying knowledge that performance and capacity planners will need in order to approach their tasks using true quantitative methods. The six chapters in this part of the book cover the following topics in detail, and are clearly and succinctly written: Markov models, single queue systems, single class MVA (Mean Value Analysis), queuing models with multiple classes, queuing models with load dependent devices, and non product-form queuing models.
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Comment 13 of 13 people found this helpful. Was this review helpful to you? Yes No Sending feedback...
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"Performance by Design" provides both a conceptual and a practical framework for experienced software developers that want to get started building quality applications using performance engineering techniques.
Performance engineering is a discipline that attempts to integrate concerns about the responsiveness of computer applications and their capacity requirements into standard application development practices, which otherwise focus almost exclusively on meeting functional requirements. Just like not getting the functional spec right in the early stages of the application development lifecycle can lead to a cascading series of design and implementation decisions that are difficult to reverse in later stages of the development process, neglecting performance considerations until after the applications has met its functional requirements is often too late to tackle them effectively.
The first part of the book surveys a wide range of performance modeling and capacity planning techniques, served up in clear, concise language with a minimum of mathematics. It is a gentle introduction to analytic queuing networks written at the level that any advanced undergraduate Computer Science student ought to be able to master. The heart of the book, representing Chapters 5 through 9, is a series of Case Studies that rounds out and concludes Part 1. Each of the case studies deftly illustrates another analytic technique that a performance engineer needs to understand how to apply. Chapter 5, for instance, steps through descriptive statistics and cluster analysis as it discusses what is involved in deriving model parameters for a simple database transaction workload.
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Comment 14 of 15 people found this helpful. Was this review helpful to you? Yes No Sending feedback...
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Capacity and Performance management is a very complex subject. I have read a couple of books. Most of them had dry theory without supporting examples. In some cases, I abruptly stopped reading. These type of books may be good to people who are quite fresh from the academic world. Having worked for sometime, one would desire a book with simple but strong fundamentals and more of relating examples.

This book stood out to my quest. The pace of the coverage was gradual from Gear 1 to Overdrive. Every ounce of theory was supported with examples. Normally I would skip theory and look for examples. But here I enjoyed reading theory. Well Written!

The Case Studies were real world examples. I gained a lot reading this book. Would recommend this book for Technology professionals who want to switch to Capacity and Performance Management.

I would definitely want Mr Menasce and his team to write books on the same topic to address real world end-to-end and new challenges like Petri Nets, Technology Consolidation, Data Warehousing, GRID, Utility Computing, Virtualisation etc. This should definitely help the Technology Community at large.
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I purchased this book on sight, having had Dr Menasce's class. It is a reasonably easy introduction to his brand of performance modeling. I don't understand why this approach isn't more well known.

His operating systems class was one of the most memorable that I took at GMU (over a decade ago). I don't know how many other Operating Systems professors take his approach in focusing on queuing theory in modeling performance problems, but his approach is enlightening.

Using one of his performance models, we were (in class) able to tweak the performance characteristics of the various (modeled) components and watch bottlenecks move from one device to another, underscoring how you can reach a point where improving performance in the wrong component can be a waste, while making small improvements in the bottleneck can provide much better (often linear) improvements.
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