Service overlay network design with reliability constraints
Bibliographic record
Abstract
We studied a class of Service Overlay Network (SON) design problem with reliability constraints. It is assumed that a SON network could enter an inadmissible status for two reasons; first when there is insufficient resource to accommodate new connections, second when some hardware devices malfunction. The design problem is usually formulated as either a Maximum Profit (MP) constrained optimization problem or a Minimum Cost (MC) constrained optimization problem. In this article we investigate the relationship between the two formulations in the context of ensuring system operability. By using the set of Lagrange multipliers from the MC formulation as a tool, we show the general condition that MP and MC give exactly the same network designs. The key contribution of this paper is the provision of insight into the solution nature of the MP and the MC formulations in designing a reliable overlay network, thereby giving guidelines to the proper formulation the network designers may consider in designing a reliable yet economically optimal SON network.
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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 | 0.000 | 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 | 0.001 | 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".