Applying parallel discrete event simulation to network emulation
Bibliographic record
Abstract
The simulation of wide area computer networks is one area where the benefits of parallel simulation have been clearly demonstrated. Here we present a description of a system that uses a parallel discrete event simulator to act as a high speed network emulator. With this, real Inter-net Protocol (IP) traffic generated by application programs running on user workstations can interact with modelled traffic in the emulator, thus providing a controlled test envi-ronment for distributed applications. The network emulator uses the TasKit conservative par-allel discrete event simulation (PDES) kernel. TasKit has been shown to be able to achieve improved parallel per-formance over existing conservative and optimistic PDES kernels, as well as improved sequential performance over an existing central-event-list based kernel. This paper ex-plains the modifications that have been made to TasKit to enable real-time operation along with the emulator inter-face that allows the IP network simulation running in the TasKit kernel to interact with real IP clients. Initial emula-tor performance data is included.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".