A Survey of PCN-Based Admission Control and Flow Termination
Bibliographic record
Abstract
Pre-congestion notification (PCN) provides feedback about load conditions in a network to its boundary nodes. The PCN working group of the IETF discusses the use of PCN to implement admission control (AC) and flow termination (FT) for prioritized realtime traffic in a DiffServ domain. Admission control (AC) is a well-known flow control function that blocks admission requests of new flows when they need to be carried over a link whose admitted PCN rate already exceeds an admissible rate. Flow termination (FT) is a new flow control function that terminates some already admitted flows when they are carried over a link whose admitted PCN rate exceeds a supportable rate. The latter condition can occur in spite of AC, e.g., when traffic is rerouted due to network failures. This survey gives an introduction to PCN and is a primer for this new technology. It presents and discusses the multitude of architectural design options in an early stage of the standardization process in a comprehensive and streamlined way before only a subset of them is standardized by the IETF. It brings PCN from the IETF to the research community and serves as historical record.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 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".