Case Study: Switching from Linux to FreeBSD
Bibliographic record
Abstract
This talk will outline the strategy used to migrate a small Canadian software company's Linux-based server infrastructure to FreeBSD. Part advocacy-strategy and part best-practices, the hope is that you'll come away with some extra tools to promote implementation of FreeBSD in your workplace. ExperiencePoint is a small (20 person) Canadian company that creates training simulations as web applications. The business is wholly dependent on its web server infrastructure for delivering its product. In 2011, I started working for ExperiencePoint and began the process of replacing its aging collection of Linux servers with a more robust FreeBSD server infrastructure. The Linux servers in question had been set up in a hurry, and the skilled software engineers who had set them up were not professional systems administrators. Linux was selected as the server operating system, but there were great opportunities for improvement and change. This talk is the story of that change. In addition to addressing the management concerns of replacing a "known" (Linux) with an "unknown" (FreeBSD), we'll explore the kinds of opportunities you should recognize in Linux environments you may come across. If you can improve reliability, reduce risk and improve performance, that's even better job security than switching to an operating system that nobody else knows.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".