Simulation of Adversarial Scenarios in OMNeT++ – Putting Adversarial Queueing Theory from Its Head to Feet
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
Adversarial models of traffic generation replace probabilis-tic assumptions by considering the deterministic worst-case. The framework of adversarial queueing theory (AQT) has discovered unexpected results on the stability of networks and has seen continuous research efforts over more than 15 years. So far, almost all AQT results have been de-rived analytically under simplifying but arguably harmless assumptions. However, as can be observed from recent work in AQT, the adversarial scenarios, in particular those that demonstrate instability, become more and more contrived and complex, thus lending themselves less and less to analyt-ical tractability. While simulation seems like a good match for this problem, no available simulation model includes ad-versarial traffic generation. In this work, we introduce an OMNeT++ simulation framework for AQT as a tool to fa-cilitate the study and development of instability examples. We validate the usefulness of AQT simulations in several use cases and, en-passant, discover some new insights into adversarial effects.
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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 | 0.002 |
| 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 | 0.002 |
| 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