Bibliographic record
Abstract
After a control plane failure, a signalling protocol needs to recover state information about the established connections in the data plane, so that new connection setups do not disrupt existing connections, and existing connections are not degraded (e.g., loss of connection information). First, we provide a comparison of existing techniques to achieve label distribution protocol (LDP) recovery. Then, we propose a backup mechanism to store state information in an upstream neighbour node. The backup LDP state information is synchronized with the original LDP state information in a downstream node when the LDP sets up or tears down connections. After that, we present a two-step LDP state information recovery, which uses a fast LDP state information recovery to recover what labels are idle before a control plane failure, and a detailed LDP state information recovery to fully recover all LDP state information. The fast LDP state information recovery is realized as part of the LDP initialization, allowing a restarting LDP session to process new connection setup requests as soon as possible, without interfering existing connections. The detailed LDP state information recovery performs in the background is parallel to the normal LDP operations. At the end, the security aspect of the proposed LDP recovery is analyzed.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".