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Analyzing Computer System Performance with Perl::PDQ [electronic resource] / by Neil J. Gunther.

By: Gunther, Neil J [author.]Contributor(s): SpringerLink (Online service)Material type: Text

TextPublisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2005Description: XXIII, 436 p. 176 illus. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783540268604Subject(s): Computer science | Computer system failures | Software engineering | Operating systems (Computers) | Management information systems | Computer Science | Software Engineering | System Performance and Evaluation | Operating Systems | Management of Computing and Information SystemsAdditional physical formats: Printed edition:: No titleDDC classification: 005.1 LOC classification: QA76.758Online resources: Click here to access online

Contents:

Theory of System Performance Analysis -- Time—The Zeroth Performance Metric -- Getting the Jump on Queueing -- Queueing Systems for Computer Systems -- Linux Load Average—Take a Load Off! -- Performance Bounds and Log Jams -- Practice of System Performance Analysis -- Pretty Damn Quick (PDQ)—A Slow Introduction -- Multicomputer Analysis with PDQ -- How to Scale an Elephant with PDQ -- Client/Server Analysis with PDQ -- Web Application Analysis with PDQ.

In: Springer eBooksSummary: Analyzing computer system performance is often regarded by most system administrators, IT professionals and software engineers as a black art that is too time consuming to learn and apply. Finally, this book by acclaimed performance analyst Dr. Neil Gunther makes this subject understandable and applicable through programmatic examples. The means to this end is the open-source performance analyzer Pretty Damn Quick (PDQ) written in Perl and available for download from the author’s Website: www.perfdynamics.com. As the epigraph in this book points out, Common sense is the pitfall of performance analysis. The performance analysis framework that replaces common sense is revealed in the first few chapters of Part I. The important queueing concepts embedded in PDQ are explained in a very simple style that does not require any knowledge of formal probability theory. Part II begins with a full specification of how to set up and use PDQ replete with examples written in Perl. Subsequent chapters present applications of PDQ to the performance analysis of multicomputer architectures, benchmark results, client/server scalability, and Web-based applications. The examples are not mere academic toys but are based on the author's experience analyzing the performance of large-scale systems over the past 20 years. By following his lead, you will quickly be able to set up your own Perl scripts for collecting data and exploring performance-by-design alternatives without inflating your manager’s schedule.

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Holdings
Item type	Current library	Call number	Status	Date due	Barcode
e-Books	Bangalore University Library		Available		BUSP001272

Analyzing computer system performance is often regarded by most system administrators, IT professionals and software engineers as a black art that is too time consuming to learn and apply. Finally, this book by acclaimed performance analyst Dr. Neil Gunther makes this subject understandable and applicable through programmatic examples. The means to this end is the open-source performance analyzer Pretty Damn Quick (PDQ) written in Perl and available for download from the author’s Website: www.perfdynamics.com. As the epigraph in this book points out, Common sense is the pitfall of performance analysis. The performance analysis framework that replaces common sense is revealed in the first few chapters of Part I. The important queueing concepts embedded in PDQ are explained in a very simple style that does not require any knowledge of formal probability theory. Part II begins with a full specification of how to set up and use PDQ replete with examples written in Perl. Subsequent chapters present applications of PDQ to the performance analysis of multicomputer architectures, benchmark results, client/server scalability, and Web-based applications. The examples are not mere academic toys but are based on the author's experience analyzing the performance of large-scale systems over the past 20 years. By following his lead, you will quickly be able to set up your own Perl scripts for collecting data and exploring performance-by-design alternatives without inflating your manager’s schedule.

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