By Neil J. Gunther

To resolve functionality difficulties in glossy computing infrastructures, frequently comprising millions of servers operating hundreds and hundreds of functions, spanning a number of stages, you wish instruments that transcend mere reporting. you wish instruments that permit functionality research of program workflow around the whole company. 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 atmosphere as buffers, and because any program structure will be represented as a circuit of queueing delays, PDQ is a typical healthy for reading method performance.

Building at the good fortune of the 1st version, this significantly increased moment version now includes 4 elements. half I includes the foundational techniques, 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 concept in a hugely intelligible sort 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 guide has been relegated to an appendix partially IV, in addition to suggestions 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 guide can be found from the author's website at

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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? , Lilja 2000], but few people take the time to carry out the analysis.

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.

See Sect. 5. D. thesis. See Chap. 4 and Sect. 1. 1967 Bill Gordon and Gordon Newell extended Jackson’s theorem to closed queueing circuits. 1973 Jeff Buzen introduces the convolution algorithm for solving closed circuits of queues. 1975 Forrest Baskett and colleagues write down the generalized BCMP rules for applying queueing theory to computer systems. See Sect. 2. 1976 Jeff Buzen introduces operational equations for Markovian queues. See Chap. 4, Sect. 4. 1977 Pierre-Jacques Courtois introduces formal concepts of hierarchical decomposition and aggregation for queueing models [Courtois 1985] [Bloch et al.

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