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

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By Neil J. Gunther

To resolve functionality difficulties in sleek computing infrastructures, frequently comprising hundreds of thousands of servers working countless numbers of functions, spanning a number of levels, you wish instruments that transcend mere reporting. you would like instruments that allow functionality research of program workflow around the complete firm. 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 surroundings as buffers, and because any software structure might be represented as a circuit of queueing delays, PDQ is a ordinary healthy for examining procedure functionality.   development at the luck of the 1st variation, this significantly increased moment version now includes 4 components. half I includes the foundational ideas, in addition to a brand new first bankruptcy that explains the primary position of queues in profitable functionality research. half II offers the fundamentals of queueing conception in a hugely intelligible kind for the non-mathematician; little greater than high-school algebra being required. half III offers many useful examples of the way PDQ should be utilized. The PDQ handbook has been relegated to an appendix partly IV, in addition to strategies to the routines contained in every one bankruptcy. all through, 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

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Cockcroft and Pettit 1998]. 9 Are Your Data Poissonian? One of the assumptions embedded in PDQ is that both interarrival and service periods are distributed exponentially (Chaps. 3, 4 and 8). Only Poisson processes can produce exponentially distributed periods. The question naturally arises, how can you determine if your data are Poisson or not? Erlang [1909] was the first to note that incoming telephone calls are exponentially distributed and this led to the development of Markovian queues like M/M/m discussed in Chap.

Most discussions about performance analysis tend to revolve around collecting performance data and we shall discuss that aspect more in Chap. 2. The next step is usually to explain those data in order to diagnose a performance problem. The tendency is to jump to conclusions based on intuition. There is nothing wrong with using intuition, but intuition is often wrong. Good performance analysis demands rigorous intuition, and rigor means testing that intuition. But testing against what? Not more data.

3). 2. Peak steady-state performance data from a production system is shown in Fig. 2. 36%. These values correspond to the dashed horizontal lines in the plot. 0199 s S= As we will show in Chaps. 4 and 5, a calculated value, like this one, for the service time S can be used as an input parameter for PDQ models. 3. 1) tells us that we may adjust the sample measurement window to remove the severity of the random fluctuations in the data. 1 Steady as She Goes 25 Fig. 2. Steady-state throughput (upper curve) and the corresponding processor utilization (lower curve) measured on a production system during a peak usage period of about three hours.

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