Performance Model Extraction Using Kernel Event Tracing
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
Models are used in performance analysis when the analyst needs to be able to predict the effect of system changes that go beyond what can be measured. The model can be obtained from a combination of system knowledge and experimentation. This thesis addresses an experimental approach to obtaining layered queueing network (LQN) models of distributed systems. It applies and extends an approach called SAME (Software Architecture and Model Extraction)which was developed to interpret application-level traces, to interpreting Kernel-level traces. Kernel-level traces have the benefit that application instrumentation is not required, and communication with attached devices can be modeled, but they lack application context information. The research shows that modeling from Kernel traces is feasible in systems which communicate via TCP messages, including Java remote procedure calls. This covers most web-based systems. Systems using middleware pose special problems. The combination of Kernel and application-level tracing was included in some experiments. Tools are described that adapt the Kernel traces to SAME, and that extract CPU demand parameter calibration information.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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