In 2010, I wrote a short blog item about Florence Nightingale the statistician, solely because of its novelty value. I didn't even bother to look closely at the associated graphic she designed, but that's what I intend to do here. In this first installment, I reflect on her famous data visualization ... [Read more...]

Call me old-fashioned, but I never saw the movie The Social Network until last year (at a private screening). In case you also missed it, it's the Hollywood version of how Facebook.com came into being. Quite apart from any artistic criticisms, I have a genuine psychological problem with movies ... [Read more...]

As already described previously, the main purpose of Release 6.0.1 Build 121512 is improved compatibility and stability between PDQ and the R statistical environment. For example, many of the PDQ models, previously found in the ../examples/ directory, can now also be accessed via the demo() command in the R-console. Testing was carried ... [Read more...]

PDQ (Pretty Damn Quick) version 6.0.β is in the QA pipeline. Although this is a major release, cosmetically, things won't look any different when it comes to writing PDQ models. All the big changes have taken place under the hood in order to make PDQ more consistent with the R statistical ... [Read more...]

In 2010, I presented my universal scalability law (USL) at the SURGE conference. I came away with the impression that nobody really understood what I was talking about (quantifying scalability) or, maybe DevOps types thought it was all too hard (math). Since then, however, I've come to find out that people ... [Read more...]

This question arose while addressing Comments on a previous blog post about exponentially distributed delays. One of my ongoing complaints is that many, if not most, popular load-test generation tools do not provide exponential variates as part of a library of time delays or think-time distributions. Not only is this ... [Read more...]

I finally got around to completing item 5 on my 2011 list concerning electrical power consumed by a magnetic hard disk drive (HDD). The semi-empirical statement is: Power ∝ Nplatters × Ω2.8 × D4.6    . . .    (1) where Nplatters is the number of platters on the spindle, Ω is the rotational speed in revolutions per minute (RPM) and D the ... [Read more...]

The ability to visualize data, enabled by the advent of graphical computer tools, has been a great boon to Cap and Perf. The power derives from the way graphical displays provide an efficient impedance match to the visual system in our brain. The weakness derives from the way graphical displays ... [Read more...]

No doubt you've heard about the tyranny of the 9s in reference to computer system availability. You're probably also familiar with the phrase six sigma, either in the context of manufacturing process quality control or the improvement of business processes. As we discovered in the recent Guerrilla Data Analysis Techniques ... [Read more...]

As usual, the Guerrilla Data Analysis Techniques (GDAT) class was a total blast. Motivated students always guarantee that. It would really help our scheduling, however, if people didn't wait until the last nanosecond to register for the class. But give... [Read more...]

I'm in the process of putting together some slides on how to apply Quantile-Quantile plots to performance data. Q-Q plots are a handy tool for visually inspecting how well your data matches a known probability distribution (prob dsn). If the match is g... [Read more...]

The title of chapter 5 in my Guerrilla Capacity Planning book is, "Evaluating Scalability Parameters," and underneath it you'll see this quote:"With four parameters I can fit an elephant. With five I can make his trunk wiggle." —John von NeumannIn that vein, Guerrilla alumnus Stephen O'C. pointed me at a ... [Read more...]

The Guerrilla Data Analysis Techniques training course (GDAT) will be held during the week of August 8-12 this year. As usual, the focus will be on applying R to your performance and capacity planning data, as well as how to use the PDQ-R modeling too... [Read more...]

It's impossible to know what will constitute a light bulb moment for someone else. In the recent Guerrilla classes (GBoot and GCaP), we seemed to be having many more than our usual quota of such moments. So much so, that I decided to keep a list. The first was mine. ... [Read more...]

PDQ is a library of functions that helps you to express and solve performance questions about computer systems using the abstraction of queues. The queueing paradigm is a natural choice because, whether big (a web site) or small (a laptop), all computer systems can be represented as a network or ... [Read more...]

In a recent Guerrilla CaP Group discussion, Baron S. wrote:....BS__ Using gnuplot against the dataset I gave, I get BS__    sigma   0.0207163 +/- 0.001323 (6.385%) BS__    kappa   0.000861226 +/- 5.414e-05 (6.287%) The Gnuplot output includes the errors for each of the universal scalability law (USL) coefficients. A question about the magnitude of these errors ... [Read more...]

In the recent GDAT class, confidence intervals (CI) for performance data were discussed. Their generalization to confidence bands (CB) for scalability projections using the USL model also came up informally. I showed a prototype plot but it was an ugly hack. Later requests from GDAT attendees to apply CBs to ... [Read more...]

Once you've downloaded PDQ with a view to solving your performance-related questions, the next step is getting started using it. Why not have some fun with blocks? Fun-ctional blocks, that is. Since all digital computers and network systems can be considered as a collection of functional blocks and these blocks ... [Read more...]

Although I use Excel all the time, and I strongly encourage my students to use it for performance analysis and CaP, I was forced to include a warranty disclaimer in my GCaP book because I discovered a serious numerical error while writing Appendix B. There, my intention was just to ... [Read more...]

Here's a summary of some things we learnt about applying R to computer performance and capacity planning data in the GDAT Class last week. Neural nets pkg nnet applied to CPU performance data in the Ripley and Venables book (see Section 8.10). How to do stacked plots that Jim calls "spark ... [Read more...]