Bibliographic record
Abstract
In this paper, we present MReplayer that supports ordering and replaying of execution traces of distributed systems that are developed using communicated state machine models. Despite the existing solutions that require detailed traces annotated with timestamps (logical or physical), MReplayer only requires a minimum amount of traces without timestamps. Instead, it uses model analysis techniques to order and replay the traces. MReplayer is composed of a set of engines that support an end-to-end solution to trace ordering and replay of distributed systems in three steps: first, a model of a distributed system is instrumented using model transformations to generate execution traces and broadcasts the traces either using a TCP connection or a log file. Second, static analysis of state machine models is performed to extract run-to-completion steps from them. Third, using the information collected (execution traces and rc-steps) in the previous steps, a lightweight centralized version of the distributed system is created and presented to users in a web-based application. We have implemented our approach using UML for Real-time (UML-RT) which is a language specifically designed for real-time embedded systems with soft real-time constraints. Finally, we have evaluated MReplayer against several use cases with various complexities. The result shows that MReplayer can reduce 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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.013 |
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 source (direct Gemma or distilled Codex), 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".