Simulation of large scale networks I: modelling differentiated services in conservative PDES
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
This paper explains how DiffServ has been implemented in an IP network simulator using an asynchronous conservative parallel discrete event simulation (PDES) kernel. DiffServ provides Quality of Service (QoS) functionality for IP networks and is designed to provide greater scalability and lower overhead than previous IP based QoS schemes. The paper explains the DiffServ components that have been implemented, focusing on the implementation of the preemptive network buffers required to provide DiffServ functionality. Certain optimisations possible for non-preemptive network buffers are not possible here. The paper explores which will work in the preemptive case. In particular, exploiting lookahead is more difficult leading to reduced performance in some cases. Optimisation schemes are described for two different preemptive buffering strategies and performance results demonstrating the costs of using these buffers are presented.
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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 | 0.000 |
| 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 | 0.001 |
| 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