A Methodology for Topological Design of Computer Communication Networks under Link Reliability Constraints
Bibliographic record
Abstract
This paper describes a method to design a cost effective computer communication network which employs unreliable links. The problem of selecting a capacity value for each link in a computer communication network is considered when different links have different reliabilities. The network topology and the total capacity of the network are given; a set of reliability values for the candidate links and the expected grade of service from the network are also available. The goal is to obtain the least costly feasible design where costs include both the link capacity and the link reliability. We present a general mathematical model for this problem and formulate the relevant constraint equations. The model is an improvement over our earlier work. Next, Lagrangean relaxation and subgradient optimization techniques are used to obtain an optimum solution for the model. The methodology is tested on several topologies, and, in all cases, good feasible solutions as well as tight lower bounds are obtained.
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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 | 0.006 | 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".