Improving Scalability of Network Emulation through Parallelism and Abstraction
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
One approach to network emulation involves simulating a virtual network with a real-time network simulator and providing an I/O interface that enables interaction between real hosts and the virtual network. This allows real protocols and applications to be tested in a controlled and repeatable environment. To reflect conditions of large networks such as the Internet it is important that the emulation environment be scalable. This paper examines improvements in scalability of the virtual network achieved through the use of parallel discrete event simulation and simulation abstraction. Using just parallel simulation techniques, real-time emulation performance of nearly 50 million packet transmissions per second is achieved on 128 processors for a network model consisting of about 20,000 nodes. Using both parallel simulation and abstraction techniques, real-time emulation performance of nearly 500 million packet transmissions per second is achieved on 128 processors for a network model consisting of about 200,000 nodes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it