Open shortest path first routing under random early detection
Bibliographic record
Abstract
In this article, we consider a variant of Open Shortest Path First (OSPF) routing that accounts for Random Early Detection (RED), an Active Queue Management method for backbone networks. In the version of OSPF we consider in this article we only require a single network path be available between each origin and destination, a simplification of the OSPF protocol. We formulate a mixed integer non‐linear program to determine the data paths, referred to as a routing policy. We prove that determining an optimal OSPF routing policy that accounts for RED is NP‐Hard. Furthermore, in order for the generated routing policies to be real‐world implementable, referred to as realizable, we must determine weights for all arcs in the network such that solving the all‐pairs shortest path problem using these weights reproduces the routing policies. We show that determining if a set of all‐pairs routes is realizable is also NP‐Hard. Fortunately, using traffic data from three real‐world backbone networks, we are able to find realizable routing policies for these networks that account for RED, using an off‐the‐shelf solver, and policies found perform better than those used in each network at the time the data was collected.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".