Combining Petri Nets and ns-2: A Hybrid Method for Analysis and Simulation
Bibliographic record
Abstract
Network protocol performance and evaluation study is often carried out using a couple of widely used simulation toolkits, such as ns-2 and OPNET. These toolkits usually contain many built-in protocol models. Using these toolkits is very efficient due to the abundant models. However, the correctness of a protocol that interests us can never be proved by simulation itself. Petri net modeling enables us to verify the protocol of interest formally. But because of the generality of Petri nets, not many network protocol models are bundled with Petri net modeling tools. In this paper we present an innovative network simulation methodology that benefits from the interaction between ns-2 and Petri nets. A communication mechanism based on Socket Programming and a synchronization mechanism used to coordinate ns-2 and Petri nets were designed to make possible the interaction. In this paper, a new version of SACK TCP, alpha-min Paced SACK TCP, is used to illustrate the power of the proposed methodology
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".