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New PDF release: Analyzing Computer System Performance with Perl::PDQ

By Neil J. Gunther

ISBN-10: 3642225829

ISBN-13: 9783642225826

To resolve functionality difficulties in smooth computing infrastructures, frequently comprising millions of servers working 1000s of purposes, spanning a number of degrees, you wish instruments that transcend mere reporting. you wish instruments that allow functionality research of program workflow around the whole company. That's what PDQ (Pretty rattling fast) offers. PDQ is an open-source functionality analyzer in accordance with the paradigm of queues. Queues are ubiquitous in each computing setting as buffers, and because any software structure might be represented as a circuit of queueing delays, PDQ is a normal healthy for studying procedure functionality.   construction at the good fortune of the 1st version, this significantly accelerated moment version now includes 4 elements. half I includes the foundational options, in addition to a brand new first bankruptcy that explains the relevant position of queues in winning functionality research. half II offers the fundamentals of queueing concept in a hugely intelligible variety for the non-mathematician; little greater than high-school algebra being required. half III offers many useful examples of ways PDQ could be utilized. The PDQ guide has been relegated to an appendix partly IV, in addition to options to the routines contained in each one bankruptcy. all through, the Perl code listings were newly formatted to enhance clarity. The PDQ code and updates to the PDQ guide can be found from the author's website at

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Additional info for Analyzing Computer System Performance with Perl::PDQ

Sample text

3. 1). One significant advantage that commercial analysis and prediction tools offer over PDQ is that they collect workload service times (Chap. 4) and build the queueing models automatically. 9 Are Your Data Poissonian? 8 How Accurate Are Your Data? In the rush to judge the accuracy of predictions made by performance tools like PDQ, the question about how accurate the measurement data is usually remains unasked. All performance measurement comes with errors. Do you know how big those errors are?

We try to minimize the impact of those fluctuations by looking for a steady state measurement period. 1 (Steady State). Denote by A the requests arriving into a system from the Internet, a production Web site or client scripts on a loadtest platform. Similarly, denote by C the number requests that are successfully completed. 1) which means that the difference between the number of arriving requests and successful completions can be made arbitrarily small. An important but hidden feature of benchmark results like those plotted in Fig.

3. 2368 Looking up the relevant K-S statistics in tables can be extremely inconvenient. 9 Are Your Data Poissonian? 1. Kolmogorov-Smirnov test #! 0; for ($j = 1; $j <= $num; $j++) { $fn = $j / $num; $ff = $expCDF[$j-1]; $Dt = max(abs($fo - $ff), abs($fn - $ff)); if ($Dt > $D) { $D = $Dt }; $fo = $fn; } $K = sqrt($num) * $D; $pvalue = 1 - exp(-2 * $K**2) * ( 1 - 2 * $K / (3 * sqrt($num))); ## Print the results print "Data : "; printdata(@data); print "Ranked: "; printdata(@sorted); print "ExpCDF: "; printdata(@expCDF); print "\n" .

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Analyzing Computer System Performance with Perl::PDQ by Neil J. Gunther

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