Virtual topology design for flexible periodic traffic demands
Bibliographic record
Abstract
In recent years, two possible approaches have been investigated for solving the virtual topology design problem for periodic (multi-hour) traffic demands. The first approach attempts to design a static topology that can accommodate all the traffic variations over time. The second option is to determine an appropriate series of virtual topologies to accommodate the different traffic loads at different times. This can lead to some cost savings in terms of the number of transceivers needed, but it requires the use of costly reconfigurable switching equipment. In this paper we present an integer linear program (ILP) formulation for designing an optimal, stable virtual topology for time-varying demands. Unlike previous work in the area, we consider the case where the exact start and end times of the demands are not known beforehand, but can slide within a larger time window. Our novel design approach integrates the topology design problem with proper scheduling of the demands in time, to create an optimal static topology capable of handling the set of periodic traffic demands.
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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 | 0.001 |
| 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".