Performance Analysis of Linux RNG in Virtualized Environments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We consider performance of Linux Random Number Generator(RNG) in virtualized environments and ask, (i) if the emulated hardware can provide sufficient entropy sources for the RNG and, (ii) if the RNG output of the host and the guest are isolated. These are important questions because insufficient entropy results in {\em entropy starvation}, and the lack of isolation results in the host and the guest RNG output to be correlated. We give detailed comparison of the Linux RNGs that run on a host and a guest in different settings. Our results show that, as expected, hosts have higher entropy sources available and generate entropy at a higher rate (entropy bit per second). We also show that generating disk activity at high rate on the guest results in a significant flow of events from the guest to the host that could possibly be exploited by an adversary to find the output of the host RNG by controlling the guest.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it