Fine-grained multilayer virtualized systems analysis
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
With the consolidation of computer services in large cloud-based data centers, almost all applications and even application development execute in virtualized systems (VS’s), sometimes nested. Whether it is inside a container, a virtual machine (VM) running on a physical host, or in a nested virtual machine, every process eventually runs on a physical CPU. Consequently, multiple virtualized systems might unknowingly compete with each other for physical resources. In this paper we study the interactions between all the VS’s running on a physical machine. We introduce an analysis based on kernel tracing that erases the bounds between VS’s and their host, to display a multilayer system as a single layer. As a result, it becomes possible to know exactly which process is currently running on a physical CPU, even if it is launched inside multiple layers of containers, themselves enclosed into two layers of VMs. To use this analysis, we developed in Trace Compass a view that displays a time line for each host CPU, showing across time which process is running. Moreover, the full hierarchy of the VS’s is retrieved from the analysis and is displayed in the view. By using a system of dynamic and permanent filters, we added the possibility to highlight in this view either traced VMs, virtual CPUs, specific processes and containers. This last feature, combined with our view, allows to thoroughly apprehend the execution flow on the physical host, although it may involve multiple nested virtualized systems.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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