Quantifying Artifacts of Virtualization: A Framework for Mirco-Benchmarks
Bibliographic record
Abstract
One of the novel benefits of virtualization is its ability to emulate many hosts with a single physical machine. This approach is often used to support at-scale testing for large-scale distributed systems. To better understand the precise ways in which virtual machines differ from their physical counterparts, we have started to quantify some of the timing artifacts that appear to be common to two modern approaches to virtualization. Here we present several systematic experiments that highlight four timing artifacts, and begin to decipher their origins within virtual machine implementations. These micro-benchmarks serve as a means to better understand the mappings that exist between virtualized and real-world testing infrastructure. Our goal is to develop a reusable framework for micro-benchmarks that can be customized to quantify artifacts associated with specific cluster configurations and workloads. This type of quantification can then be used to better anticipate behavioral characteristics at-scale in real settings.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".