Intercarrier bandwidth exchange: an engineering framework
Bibliographic record
Abstract
A mismatch between demand and supply for bandwidth is common in transport carrier networks. This mismatch is generally a result of the disparity between a carrier's capacity buildout and its anticipated customer demand. A carrier with temporary bandwidth deficit or lack of presence in a geographical region and a carrier with surplus capacity in the right locations can be brought together by the emerging bandwidth exchange technology. Bandwidth exchange offers a win-win solution, in which the carrier with a deficit avoids losing revenue by buying capacity from the carrier with surplus, and the latter makes additional revenue by retail sale of its excess capacity. While the concept of real-time purchase and exchange of bandwidth has attracted a lot of attention, many technical challenges stand in the way of making it a reality. The purpose of this article is to provide an engineering framework for enabling real-time bandwidth exchange with committed quality of service and service level agreement among transport carriers. Special emphasis is given to identifying the architectural requirements and the enabling infrastructure necessary for building a viable bandwidth exchange that can be used for creating revenue out of surplus stranded capacity. Indepth analysis of cross-carrier service level agreement specification, capacity publication, route design, and service provisioning are also provided in the article.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".