Modelling and simulation-based design of a distributed devs simulator
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
Distributed, discrete-event simulators are typically deployed on different computing and network platforms using different implementation languages. This hampers realistic performance comparisons between simulator implementations. Furthermore, algorithms used are typically only present in code rather than explicitly modeled. This prohibits rigorous analysis and re-use. In this paper, the structure and behavior of a distributed simulator for the DEVS formalism is modeled explicitly, in the DEVS formalism. Simulation of this model of the simulator allows for the quantitative analysis of reliability and performance of different alternative simulator designs. In particular, using a model of a distributed simulator allows one to simulate scenarios such as failures of computational and network resources, which can be hard to realize in reality. We demonstrate our model-based approach by modeling, simulating and ultimately synthesizing a distributed DEVS simulator. Our goal is to achieve fault tolerance whilst optimizing performance.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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