Efficient reordering and replay of execution traces of distributed reactive systems in the context of model-driven development
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
Ordering and replaying of execution traces of distributed systems is a challenging problem. State-of-the-art approaches annotate the traces with logical or physical timestamps. However, both kinds of timestamps have their drawbacks, including increased trace size. We examine the problem of determining consistent orderings of execution traces in the context of model-driven development of reactive distributed systems, that is, systems whose code has been generated from communicating state machine models. By leveraging key concepts of state machines and existing model analysis and transformation techniques, we propose an approach to collecting and reordering execution traces that does not rely on timestamps. We describe a prototype implementation of our approach and an evaluation. The experimental results show that compared to reordering based on logical timestamps using vector time (clocks), our approach reduces the size of the trace information collected by more than half while incurring similar runtime overhead.
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 | 0.000 |
| 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