Neil J. Gunther

To solve performance problems in modern computing infrastructures, often comprising thousands of servers running hundreds of applications, spanning multiple tiers, you need tools that go beyond mere reporting. You need tools that enable performance analysis of application workflow across the entire enterprise. That's what PDQ (Pretty Damn Quick) provides. PDQ is an open-source performance analyzer based on the paradigm of queues. Queues are ubiquitous in every computing environment as buffers, and since any application architecture can be represented as a circuit of queueing delays, PDQ is a natural fit for analyzing system performance. Building on the success of the first edition, this considerably expanded second edition now comprises four parts. Part I contains the foundational concepts, as well as a new first chapter that explains the central role of queues in successful performance analysis. Part II provides the basics of queueing theory in a highly intelligible style for the non-mathematician; little more than high-school algebra being required. Part III presents many practical examples of how PDQ can be applied. The PDQ manual has been relegated to an appendix in Part IV, along with solutions to the exercises contained in each chapter.Throughout, the Perl code listings have been newly formatted to improve readability. The PDQ code and updates to the PDQ manual are available from the author's web site at www.perfdynamics.com

To solve performance problems in modern computing infrastructures, often comprising thousands of servers running hundreds of applications, spanning multiple tiers, you need tools that go beyond mere reporting. You need tools that enable performance analysis of application workflow across the entire enterprise. That's what PDQ (Pretty Damn Quick) provides. PDQ is an open-source performance analyzer based on the paradigm of queues. Queues are ubiquitous in every computing environment as buffers, and since any application architecture can be represented as a circuit of queueing delays, PDQ is a natural fit for analyzing system performance. Building on the success of the first edition, this considerably expanded second edition now comprises four parts. Part I contains the foundational concepts, as well as a new first chapter that explains the central role of queues in successful performance analysis. Part II provides the basics of queueing theory in a highly intelligible style for the non-mathematician; little more than high-school algebra being required. Part III presents many practical examples of how PDQ can be applied. The PDQ manual has been relegated to an appendix in Part IV, along with solutions to the exercises contained in each chapter.Throughout, the Perl code listings have been newly formatted to improve readability. The PDQ code and updates to the PDQ manual are available from the author's web site at www.perfdynamics.com

In these days of shortened fiscal horizons and contracted time-to-market schedules, traditional approaches to capacity planning are often seen by management as tending to inflate their production schedules. Rather than giving up in the face of this kind of relentless pressure to get things done faster, Guerrilla Capacity Planning facilitates rapid forecasting of capacity requirements based on the opportunistic use of whatever performance data and tools are available in such a way that management insight is expanded but their schedules are not. A key Guerrilla concept is tactical planning whereby short-range planning questions and projects are brought up in team meetings such that management is compelled to know the answer, and therefore buys into capacity planning without recognizing it as such. Once you have your "foot in the door", capacity planning methods can be refined in an iterative cycle of improvement called "The Wheel of Capacity Planning". Another unique Guerrilla tool is Virtual Load Testing, based on Dr. Gunther's "Universal Law of Computational Scaling", which provides a highly cost-effective method for assessing application scalability.

In these days of shortened fiscal horizons and contracted time-to-market schedules, traditional approaches to capacity planning are often seen by management as tending to inflate their production schedules. Rather than giving up in the face of this kind of relentless pressure to get things done faster, Guerrilla Capacity Planning facilitates rapid forecasting of capacity requirements based on the opportunistic use of whatever performance data and tools are available in such a way that management insight is expanded but their schedules are not. A key Guerrilla concept is tactical planning whereby short-range planning questions and projects are brought up in team meetings such that management is compelled to know the answer, and therefore buys into capacity planning without recognizing it as such. Once you have your "foot in the door", capacity planning methods can be refined in an iterative cycle of improvement called "The Wheel of Capacity Planning". Another unique Guerrilla tool is Virtual Load Testing, based on Dr. Gunther's "Universal Law of Computational Scaling", which provides a highly cost-effective method for assessing application scalability.