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.
Footnotes
- According to Computer World in 1999, a 32-node IBM SP2 cost $2 million to lease over 3 years. This SP2 cluster was about 6 times bigger.
- Because of my vain attempt to suppress details (in the interests of video length), Boris gets confused about the kind of files that are causing the performance problem (near 26:30 minutes). They're not regular data files and they're not executable files. The executable is already running but sometimes waits—for a long time. The question is, waits for what? They are, in fact, special font files that are requested by the X-windows application (the client, in X parlance). These remote files may also get cached, so it's complicated. In my GCAP class, I have more time to go into this level of detail. Despite all these potential complications, my 'log model' accurately predicts the mean application launch time.
- Log_2 assumes a binary tree organization of font files whereas, Log_10 assumes a denary tree.
- Question for the astute viewer. Since these geophysics applications were all developed in-house, how come the developers never saw the performance problems before they ever got into production? Here's a hint.
- Some ppl have asked why there's no video of me. This was the first time Boris had recorded video of a Skype session and he pushed the wrong button (or something). It's prolly better this way. :P
