A framework for MPLS path setup in unidirectional multicast shared trees
Bibliographic record
Abstract
Establishing multicast communications in MPLS-capable networks is an essential requirement for a wide-scale deployment of MPLS in the Internet. This paper outlines a framework for the setup of a MultiPoint-to-MultiPoint (MP2MP) Label Switched Path (LSP) for establishing uni-directional multicast shared trees. The presented framework 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 manner similar to PIM-SM shared trees. Senders of the multicast session have to register with the RP and establish unicast LSPs with the RP. Receivers who join the session have to send their join requests to the RP which acts as a root (and the sender) of a one-to-many tree by establishing a Point-to-MultiPoint (P2MP) LSP between the RP and the receiver. This architecture utilizes more than one RP to implement RP failure recovery, to provide load balancing within the domain, and to enable the extension of this framework to multiple domains by establishing LSPs between RPs in different domains. This architecture also 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 paper explains the framework in details and provides an example on how to set the LSP on a given topology. We also refer to some preliminary simulation results testing the scalability of the architecture in comparison with traditional multicast routing.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".