Re-routing using Contraction Hierarchies in Software-Defined Networks
Bibliographic record
Abstract
According to the Open Networking Foundation (ONF), one of the reasons to reexamine traditional network architectures is the increment of mobile devices and its data transmission. The global IP traffic forecast by CISCO estimates an overall traffic increase to 396 exabytes per month in 2022, more than three times the traffic on 2017 (122 exabytes per month). In this work, we research the similarities between vehicular networks and computer networks. These similarities will allow us to implement the Contraction Hierarchies algorithm (CH) in computer networks. CH is an interdisciplinary algorithm from vehicular networks which can provide us with the elements and logic to optimize specific routing problems in computer networks. In order to implement CH, we use Software Defined Networks (SDN). SDN is a computer networks paradigm that separates the Data and Control planes. The Data plane is left to the network devices to distribute the packages, and the control plane is centralized into a Controller. By having a controller with a broad view of the network, we implement CH in order to optimize route selection. Once the route is determined, we study the possibility of using the advantages of CH to redistribute traffic in case the network elements suffer from unforeseen circumstances.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".