Studying performance of DEVS modeling and simulation environments using the DEVStone benchmark
Bibliographic record
Abstract
The Discrete Event System Specification (DEVS) formal modeling and simulation (M&S) framework (which supports hierarchical and modular model composition) has been widely used to understand, analyze and develop a variety of systems. Numerous DEVS simulators have been developed; nevertheless, evaluating the performance of such simulators is a complex process and it has been usually done using ad hoc methods. DEVStone, instead, is a synthetic benchmark that can be used to automate the evaluation of the performance of DEVS-based simulators. DEVStone generates a suite of models with varied structure and behavior automatically. To do so, it uses a standardized mechanism that can be the basis for comparisons between simulation software tools. As a proof of the concept, we present various tests in which DEVStone was used to study the efficiency of five different simulation engines. In this case, we compared various versions of the CD++ simulator, and then compared its performance with the ‘A Discrete Event System Simulator’ (ADEVS) M&S tool. This is the first effort in which these simulation tools have been thoroughly compared with a very demanding set of experiments. The use of DEVStone allowed a standardized and exhaustive method to compare different features of the simulation software. We show how the basic ideas used for DEVStone facilitates performance analysis for upgrades and updates of a given simulation engine, while also providing a common metric to compare different M&S environments.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".