The Importance of 2 + ε Dimensions: Flat is the Name of the Game

In a 2010 email, I wrote the following about Steve Jobs:

Sent: Mon, May 31, 2010 5:02:32 PM
Subject: Why Jobs has been vindicated on quality

Observation: Jobs has finally been vindicated on his stand over high quality (and premium price, although not as premium as it used to be). Why?

Theory: Jobs has made the computer 2-dimensional.

Data: From the earliest days of the Mac, Jobs preached quality (there's even a video clip with him slagging Gates for failing to understood quality). For 2 decades Jobs was proven wrong, in the sense that customers were not willing to pay a premium for quality, so Apple never garnered more than 4-5% of the PC market.

What happened at HealthCare.gov?

On Oct. 6th Federal officials admitted the online marketplace needed design changes, as well as more server capacity to improve efficiency on the federally run exchange that serves 36 states. More details in this WSJ article.

And finally, from the PR horse's mouth on Oct 20th:

"Initially, we implemented a virtual 'waiting room,' but many found this experience to be confusing. We continued to add more capacity in order to meet demand and execute software fixes to address the sign up and log in issues, stabilizing those parts of the service and allowing us to remove the virtual 'waiting room.' "

Quite apart from the bizarre architectural description, a "virtual waiting room" implies a buffer or buffers where pending requests must wait for service because the necessary resources to complete those requests are not available due to being either busy or failed. A certain amount of waiting time can be tolerated by users (both applicants and providers) but if it becomes too long or simply fails to complete, that kind of poor performance points to grossly under-scaled capacity in the original design.

Laplace the Bayesianista and the Mass of Saturn

I'm reviewing Bayes' theorem and related topics for the upcoming GDAT class. In its simplest form, Bayes' theorem is a statement about conditional probabilities. The probability of A, given that B has occurred, is expressed as: \begin{equation} \Pr(A|B) = \dfrac{\Pr(B|A)\times\Pr(A)}{\Pr(B)} \label{eqn:bayes} \end{equation} In Bayesian language, $\Pr(A|B)$ is called the posterior probability, $\Pr(A)$ the prior probability, and $\Pr(B|A)$ the likelihood (essentially a normalization factor).

Source: Wikipedia

GDAT Class October 14-18, 2013

This is your fast track to enterprise performance and capacity management with an emphasis on applying R statistical tools to your performance data.

Early-bird discounts are available for the Level III Guerrilla Data Analysis Techniques class Oct 14—18.

As usual, all classes are held at our lovely Larkspur Landing location in Pleasanton. Attendees should bring their laptops to the class as course materials are provided on a flash drive. Larkspur Landing also provides free wi-fi Internet in their residence-style rooms as well as the training room.

What's Wrong with This Picture?

Here are some examples of how not to display performance data. Remember: collecting and analyzing your performance data is only half the battle. The other, equally difficult, half is presenting your performance data and conclusions.

Example 1

This first example is an oldie but a baddie. It will also provide some context for the second example below.

Figure 1 (click to enlarge)

See the problem?

Capacity Planning Classes in August 2013

Bookings are open for both Guerrilla Boot Camp (GBoot) and Guerrilla Capacity Planning (GCaP) classes in August 2013 at the Early Bird rate.

As usual, classes will be held at our lovely Larkspur Landing location. Click on the image for booking information. Here are some comments contributed by Guerrilla alumni.

Attendees should bring their laptops, as course materials are provided on CD or flash drive. The venue also offers free wi-fi to the internet.

Exponential Cache Behavior

Guerrilla grad Gary Little observed certain fixed-point behavior in simulations where disk IO blocks are updated randomly in a fixed size cache. For his python simulation with 10 million entries (corresponding to an allocation of about 400 MB of memory) the following results were obtained:

• Hit ratio (i.e., occupied) = 0.3676748
• Miss ratio (i.e., inserts) = 0.6323252

In other words, only 63.23% of the blocks will ever end up inserted into the cache, irrespective of the actual cache size. Gary found that WolframAlpha suggests the relation: \begin{equation} \dfrac{e-1}{e} \approx 0.6321 \label{eq:walpha} \end{equation} where $e = \exp(1)$. The question remains, however, where does that magic fraction come from?