PARALLEL SIMULATION OF ATM NETWORKS: CASE STUDY AND LESSONS LEARNED
Bibliographic record
Abstract
This paper summarizes our experiences in developing and using a cell-level ATM network simulator called ATM-TN. The ATM-TN simulator was developed as part of TeleSim, a collaborative research project aimed at developing high performance parallel simulation tools for the design and analysis of broadband ATM networks. The ATM-TN simulator provides the fundamental platform for ongoing research in two areas: parallel simulation performance (e.g., optimistic synchronization, partitioning, dynamic load balancing) and ATM network performance (e.g., traffic modeling, ATM switch design, ABR traffic control). Our experiences with the simulator to date have been largely positive. On the parallel simulation front, we have found that ATM network simulation is a promising application domain for parallel simulationtechniques, though there are significant technical challenges to overcome regarding event granularity, simulation partitioning, scheduling, and load balancing. On the network performance f...
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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 | 0.001 | 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".