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

By Neil J. Gunther

To resolve functionality difficulties in smooth computing infrastructures, frequently comprising millions of servers working 1000s of purposes, spanning a number of levels, you wish instruments that transcend mere reporting. you would like instruments that allow functionality research of software 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 setting as buffers, and because any program structure should be represented as a circuit of queueing delays, PDQ is a usual healthy for reading method performance.


Building at the luck of the 1st version, this significantly elevated moment version now contains 4 components. half I includes the foundational strategies, in addition to a brand new first bankruptcy that explains the critical function of queues in profitable functionality research. half II offers the fundamentals of queueing concept in a hugely intelligible kind for the non-mathematician; little greater than high-school algebra being required. half III provides many sensible examples of ways PDQ should be utilized. The PDQ handbook has been relegated to an appendix partly IV, besides strategies to the routines contained in each 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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Sample text

To maintain data consistency, requests must be granted in the order in which they were issued. 5 Logical Clocks Although physical time can be maintained to accuracies of a few tens of milliseconds using protocols such as NTP [Mills 1992], this is not adequate for capturing process precedence in distributed systems, which can occur on microsecond timescales. However, in a distributed computation, both progress toward a common goal and the interprocess communication synchrony can be accommodated using logical clocks.

How can we picture the sudden onset of this kind of performance collapse?

17) is constant. 15) becomes the exponential reliability function: R(t) = e−λ t . 16) produces: M T BF = 1 . 19) In other words, the MTBF is just the inverse of the failure rate. The main feature of exponential models is that they represent system failures that are statistically independent or random in time, which is also known as the “ageless” property. Weibull distribution. This is an alternative model specified by two parameters. The instantaneous failure rate is: h(t) = λ αtα−1 . 20) Parameter values of α = 1 represent various degrees of aging or fatiguing.

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