By Neil J. Gunther
To remedy functionality difficulties in sleek computing infrastructures, usually comprising hundreds of thousands of servers operating hundreds and hundreds of purposes, spanning a number of degrees, you wish instruments that transcend mere reporting. you would like instruments that allow functionality research of software workflow around the complete firm. that is what PDQ (Pretty rattling speedy) presents. PDQ is an open-source functionality analyzer in response to the paradigm of queues. Queues are ubiquitous in each computing atmosphere as buffers, and because any program structure could be represented as a circuit of queueing delays, PDQ is a usual healthy for studying process performance.
Building at the luck of the 1st variation, this significantly elevated moment version now includes 4 elements. half I includes the foundational thoughts, in addition to a brand new first bankruptcy that explains the imperative function of queues in profitable functionality research. half II presents the fundamentals of queueing conception in a hugely intelligible type for the non-mathematician; little greater than high-school algebra being required. half III offers many useful examples of ways PDQ might be utilized. The PDQ handbook has been relegated to an appendix partially IV, in addition to strategies to the routines 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 site at www.perfdynamics.com
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Additional info for Analyzing Computer System Performance with Perl::PDQ
Eventually it will be understood. What we do know at this point is that something is broken in the measurement process and it has to be resolved or any further load testing is simply a waste of time and resources. Notice that I am saying the data are the problem, not the model. The model, at this stage of the analysis, is nothing more than a set of ratios for which the physical meaning is very clear and unambiguous. We’re not even using any sophisticated modeling techniques at this point, so, it cannot be the model.
On the other hand, if we ignore this problem and simply press on regardless, we run into trouble in the fourth column in Fig. 2 with negative values for the deviation from linearity. Once again, this is easier to see if we plot it (Fig. 4) and use the notation L(N ) = (N/C) − 1 on the y-axis. The requirement is that all these values be positive. Except for the first data point, all the other data points in the dark area are negative and therefore illegal. 6 0 50 100 150 200 250 300 350 N Fig. 3.
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. Like JIT, will this algorithm also find its way into improved computer performance?