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

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By Neil J. Gunther

To clear up functionality difficulties in smooth computing infrastructures, usually comprising hundreds of thousands of servers working 1000s of purposes, spanning a number of degrees, you wish instruments that transcend mere reporting. you wish instruments that let functionality research of program workflow around the complete firm. that is what PDQ (Pretty rattling fast) presents. PDQ is an open-source functionality analyzer in line with the paradigm of queues. Queues are ubiquitous in each computing setting as buffers, and because any program structure may be represented as a circuit of queueing delays, PDQ is a usual healthy for interpreting procedure performance.

Building at the luck of the 1st variation, this significantly increased moment variation now contains 4 components. half I comprises the foundational recommendations, in addition to a brand new first bankruptcy that explains the primary position of queues in winning functionality research. half II offers the fundamentals of queueing conception in a hugely intelligible variety for the non-mathematician; little greater than high-school algebra being required. half III provides many sensible examples of the way PDQ could be utilized. The PDQ guide has been relegated to an appendix partly IV, besides recommendations to the workouts contained in every one chapter.

Throughout, the Perl code listings were newly formatted to enhance clarity. The PDQ code and updates to the PDQ handbook can be found from the author's website at

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See Sects. 2. 2003 In an attempt to make PDQ more widely accessible to unix and Linux system administrators (who are often tasked with doing impromptu performance analysis), the author and Peter Harding release an open-source version of PDQ in Perl and Python. purdue. html). Similar to the Kanban concept developed by Toyota in the 1950s (see above), the largest objects that can be disassembled quickly are moved from the staging area first because it significantly reduces the amount of storage space needed.

K-S Statistics\n" . 00 1 2 3 4 5 6 Sample Fig. 7. 3 because it is calculated directly from the value of Kdata . pl. 2 generates exponential variates using a robust pseudo-random number generator. 10 Performance Measurement Standards Some performance management standards [see Gunther 2000a, Chap. 2. Exponential variate generator #! 0)); } sub exp_variate { # Return an exponential variate. # log == Ln in Perl.

Between N = 5 and N = 150 users, the efficiency values are bigger than 100%. This is absurd because you simply cannot have more than 100% of anything. It’s even easier to see the problem if we plot it (Fig. 3). The N = 1 efficiency in the third column of the Excel spreadsheet corresponds to 100% and that is exactly what we expect. Similarly, for the final value at N = 350 users, the efficiency of the system has fallen to 60% of the N = 1 efficiency. In between, however, we have some efficiency values that are in excess of 100%.

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