## Wednesday, March 14, 2018

### WTF is Modeling, Anyway?

A conversation with performance and capacity management veteran Boris Zibitsker, on his BEZnext channel, about how to save multiple millions of dollars with a one-line performance model that is accurate to within ±5% (starting around 21:50 minutes into the video). I wish my PDQ models were that good. :/

The strength of the model turns out to be its explanatory power, rather than prediction, per se. However, with the correct explanation of the performance problem in hand (which also proved that all other guesses were wrong), this model correctly predicted a 300% reduction in application response time for essentially no cost. Modeling doesn't get much better than this.

### The Original Analysis

In 2013, a Redmonk blogger claimed that the growth of GitHub (GH) users follows a certain type of diffusion model called Bass diffusion. Here, growth refers to the number of unique user IDs as a function of time, not the number project repositories, which can have a high degree of multiplicity.

In a response, I tweeted a plot that suggested GH growth might be following a power law, aka scale free growth. The tell-tale sign is the asymptotic linearity of the growth data on double-log axes, which the original blog post did not discuss. The periods on the x-axis correspond to years, with the first period representing calendar year 2008 and the fifth period being the year 2012.

## Saturday, October 8, 2016

### Crib Sheet for Emulating Web Traffic

Our paper entitled, How to Emulate Web Traffic Using Standard Load Testing Tools is now available online and will be presented at the upcoming CMG conference in November.

Session Number: 436
Subject Area: APM
Session Date: WED, November 9, 2016
Session Time: 1:00 PM - 2:00 PM
Session Room: PortofinoB

## Saturday, October 1, 2016

### A Clue for Remembering Little's Law

During the Guerrilla Data Analysis class last week, alumnus Jeff P. came up with this novel mnemonic device for remembering all three forms of Little's law for a queue with mean arrival rate $\lambda$.

The right-hand side of each equation representing a version of Little's law is written vertically in the order $R, W, S$, which matches the expression $R=W+S$ for the mean residence time, viz., the sum of the mean waiting time ($W$) and the mean service time ($S$).

The letters on the left-hand side: $Q, L, U$ (reading vertically) respectively correspond to the queueing metrics: queue-length, waiting-line length, and utilization, which can be read as the word clue.

Incidentally, the middle formula is the version that appears in the title of John Little's original paper:

J. D. C. Little, A Proof for the Queuing Formula: L = λ W,''
Operations Research, 9 (3): 383—387 (1961)