MPLS-based Multicast Shared Trees
Bibliographic record
Abstract
This paper presents a study of our proposed architecture for the setup of a multipoint-to-multipoint (MP2MP) label switched path (LSP). This form of LSP is needed for establishing uni-directional multicast shared trees. Such trees are required for information distribution in applications such as video conferencing. The presented architecture is intended for multicast applications within a single autonomous domain and can be extended to cover inter-domain multicast sessions. We propose the use of one (or more) control points in the network called Rendez-vous points (RP) in a simple extention may utilize more than one RP to implement RP failure the PIM-SM protocol to implement multicast in MPLS networks. This architecture has the advantage of using existing MPLS techniques and existing routing protocols and requires only the addition of more management capabilities at the RPs. The experiments we carried out show that while retaining the advantages of using MPLS over traditional multicast routine, the performance of the new architecture is comparable to that of IP multicast in terms of the volume of control messages and label and memory consumption. Also the architecture scales well with the increase of the number of senders within a multicast group and with the increase of the number of multicast groups.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".