Bibliographic record
Abstract
In this paper, a novel cost model for active networks is proposed. Active networks allow bandwidth-processing tradeoff by introducing processing at intermediate nodes inside the network. The flows travelling through active nodes use the processing resources at the node to adapt to the current conditions of the network. For example, a flow may compress to avoid traffic congestion in a heavily loaded region of the network, or to minimize the cost of transmission. We first argue that the current network cost models are not sufficient for analyzing the bandwidth-processing tradeoff because: 1) they do not associate any cost with the processing performed at the node, and 2) flows in active networks are no longer of constant rate throughout the route from origin to destination. We then introduce a novel cost model suitable for active network cost analysis that takes into account the additional expense of processing at active nodes. We demonstrate the advantages of this cost model over traditional models by implementing schemes that perform connection admission, reservation and routing in an active network using the new model. We show that the traditional models and routing schemes, when used for active networks, yield non-optimal results. The new cost model allows for quantitative tradeoff between bandwidth and processing leading to optimal routing and reservation decisions.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".