Visual Proof of Little's Law Reworked

Back in early March, when I was at the Hotsos Symposium on Oracle performance, I happened to end up sitting next to Alain C. at lunch. He always attends my presentations, especially on USL scalability analysis. During our lunchtime conversation, he took out his copy of Analyzing Computer System Performance with Perl::PDQ and opened it at the section on the visual proof for Little's law. Alain queried (query ... Oracle ... Get it?) whether the numbers really added up the way they are shown in the diagrams. It did look like there could be a discrepancy but it was too difficult to reanalyze the whole thing over lunch.

Book Review: Botched Erlang B and C Functions

As a consequence of looking into a question on the GCaP google group about the telecom performance metric known as the busy hour, I came across this book.

A new copy comes with a $267.44 price tag!!! Here is my review; slightly enhanced from the version I wrote on Amazon.

Adding Percentiles to PDQ

Pretty Damn Quick (PDQ) performs a mean value analysis of queueing network models: mean values in; mean values out. By mean, I mean statistical mean or average. Mean input values include such queueing metrics as service times and arrival rates. These could be sample means. Mean output values include such queueing metrics as waiting time and queue length. These are computed means based on a known distribution. I'll say more about exactly what distribution, shortly. Sometimes you might also want to report measures of dispersion about those mean values, e.g., the 90th or 95th percentiles.

Percentile Rules of Thumb

In The Practical Performance Analyst (1998, 2000) and Analyzing Computer System Performance with Perl::PDQ (2011), I offer the following Guerrilla rules of thumb for percentiles, based on a mean residence time R:

• 80th percentile: p80 ≃ 5R/3
• 90th percentile: p90 ≃ 7R/3
• 95th percentile: p95 ≃ 9R/3

I could also add the 50th percentile or median: p50 ≃ 2R/3, which I hadn't thought of until I was putting this blog post together.

Upcoming GDAT Class May 6-10, 2013

Enrollments are still open for the Level III Guerrilla Data Analysis Techniques class to be held during the week May 6—10. Early-bird discounts are still available. Enquire when you register.

As usual, all classes are held at our lovely Larkspur Landing location. Before registering online, take a look at what former students have said about the Guerrilla courses.

Attendees should bring their laptops, as course materials are provided on CD or flash drive. Larkspur Landing also provides free Internet wi-fi in all rooms.

The Most Important Scatterplot Since Hubble?

In 1929, the astronomer Edwin Hubble published the following scatterplot based on his most recent astronomical measurements.

Figure 1. Edwin Hubble's original scatterplot

It shows the recession velocity of the "stars" (in km/s) on the y-axis and their corresponding distance (in Megaparsecs) on the x-axis. A Megaparsec is about 3.25 million light-years. This scatterplot is important for several reasons:

Harmonic Averaging of Monitored Rate Data

The following slides constitute evolving notes made in response to remarks that arose during the Monitorama Conference in Boston MA, March 28-29, 2013. Since they are evolving, the content will be updated continuously in place. So, get on RSS or Twitter or check back often to read the latest version.

During the Graphite workshop session at Monitorama, the topic of aggregating monitored rate data came up. This caused me to interject the cautionary comment: