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Under today’s shortened fiscal horizons and contracted time-to-market schedules, traditional approaches to capacity planning are seen by management as inflating production schedules. In the face of relentless pressure to get things done faster, this book facilitates rapid forecasting of capacity requirements, based on opportunistic use of available performance data and tools so that management insight is expanded but production schedules are not. The book introduces such concepts as an iterative cycle of improvement called "The Wheel of Capacity Planning," and Virtual Load Testing, which provides a highly cost-effective method for assessing application scalability.
- Sales Rank: #1046906 in Books
- Brand: Brand: Springer
- Published on: 2006-12-19
- Original language: English
- Number of items: 1
- Dimensions: 9.21" h x .69" w x 6.14" l, 1.20 pounds
- Binding: Hardcover
- 253 pages
- Used Book in Good Condition
About the Author
Neil J. Gunther, M.Sc., Ph.D., SMIEEE, is an internationally recognized IT researcher and computer performance analyst who founded Performance Dynamics Company (www.perfdynamics.com) in 1994. Originally from Melbourne, Australia, he has resided near Silicon Valley in California since 1980. In that time Dr. Gunther has held teaching positions at California State University-Hayward and San Jose University, as well as research and management positions at Xerox PARC, Pyramid/Siemens Technology, and JPL/NASA where he worked on the Voyager and Galileo missions. His "Guerrilla Capacity Planning" classes have been presented at such organizations as America Online (AOL), Boeing, FedEx, Motorola, Nokia, Stanford University, Sun Microsystems and UCLA. In 1996, Dr.
Gunther was awarded Best Technical Paper at the Computer Measurement Group international conference (CMG'96) and at CMG'08 he received the prestigious A.A. Michelson Award---the industry's highest honor for computer performance analysis and capacity planning. Dr. Gunther is also a member of AMS, APS, ACM and SPIE. More details can be found on his Wiki page.
Most helpful customer reviews
0 of 0 people found the following review helpful.
A great read
By Maulan Byron
A great book on pragmatic techniques to do performance analysis modeling and capacity planning. If you are a developer or from the operations domain and you care about application performance and scale the this is a must read. The author is brilliant.
0 of 0 people found the following review helpful.
Outstanding - Clearly focuses on the key elements to measure capacity & performance
By John Hinkle
Neil has done a great job of defining the keys to measuring capacity requirements and techniques for getting this done when time resources are constrained.
0 of 2 people found the following review helpful.
a gem and a keeper
By metasoft
With Wall Street analysts drives the planning horizon, Management prefers getting a sense of direction quickly and repeatedly, instead of belated precise readings of compass bearing. It is in this agile and opportunistic spirit and philosophy that Dr. Gunther introduces Excel, linear regression, and 2 parameter scalability models into the performance analysts' tool chest.
Excel is ubiquitous. It is also easy to use. Use it. If there is sufficient time, better tools such as R or Mathematica can be used to cross-check Excel results. Similarly, linear regression is another tool in the agile performance analysts' tool chest.
Two chapters I have not seen presented elsewhere are the virtualization spectrum and effective demand. In a prior job, having virtualization spectrum chapter available to me would have save me much grief with an workload manager. The effective demand makes another useful capacity project tool to keep handy.
The best part is Dr. Gunther's 2 parameter universal scalability model. It can be immediately used to frame your load testing results to project application scalability. This alone is worth the cost of the book and admission to his classes.
Conjecture 4.1 on page 65 on 2 parameters are necessary and sufficient for scalability model based on rational functions are an interesting open questions. Given that the denominator is a quadratic equation with c = 1, we should be able to argue that it behaves like a parabola, except with c = 1, we won't get into singularity/infinity. For more details, please see Dr. Gunther's blog at
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