Guerrilla Capacity Planning: A Tactical Approach to Planning for Highly Scalable Applications and Services 2007th Edition
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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.
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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
A great practical handbook.
However... really all of this value was in the first quarter of the book. I read on and read on looking for further conceptual gems but they weren't to be found.
I guess that books are "meant" to be at least a particular length, but this one could have been much shorter and more concise.
The "universal scalability law" that he describes in section 4.4, and for which he provides figure 4.8 and equation 4.31, extends Amdahl's Law via the addition of a "coherency" term that models effects such as data exchange between parallel processes. And although Dr. Gunther suggests that this coherency term ought to grow linearly with the number of parallel processes, and hence should appear as a quadratic term in equation 4.31, this coherency term depends on the specific communication architecture of the computer system and can grow non-linearly, for example, as log to the base two of the number of processes.
This logarithmic growth law may occur because one processor may not communicate directly with all other processors. Instead, one processor may send information to two other processors, and each of those two processors may send information to two more processors, and so forth. Therefore, in order to model the communication that occurs in such a communication cascade, the quadratic n(n-1) coherency term in equation 4.31 should be replaced by an n*log(n) term.
Moreover, performance data that are obtained from current parallel computer systems do not always conform to Dr. Gunther's "universal" scalability "law" under other conditions. For example, a large volume of data that exceeds the capacity of the total cache memory when distributed across a few processors may well fit into total cache memory when distributed across a larger number of processors. Under these conditions, the scalability for the larger number of processors appears to grow "super-linearly" relative to the scalability of a few processors. However, Dr. Guther's model specifically disallows this "super-linear" scaling that is commonly observed. Thus, although Dr. Gunther's book is a useful introduction to the subject of measuring and modeling the behavior of parallel computer architectures, his universal scalability law should not be considered to be universal.