An approach to solving a multicasting scalability issue in MPLS networks using state encoding
Bibliographic record
Abstract
We propose an approach that handles the scalability issue in multicast communication in terms of increasing the number of IP flows over MPLS networks. The proposed scheme depends on state encoding of the interfaces of each router that a multicast tree passes through. Each router assigns a code for each multicast tree that passes through it and these state codes are collected and sent back to the ingress router. At the ingress router, the concatenation of all state codes can be considered as a code for the multicast tree that carries the data streams of the corresponding session. The ingress router uses the tree codes. to classify multicast sessions into forwarding equivalence classes (FEC). If two multicast sessions have the same code, they are classified to the same FEC and given the same label. This procedure results in a reduction in label usage and provides the capability to aggregate different multicast data streams. Compared with previous work, which maintains the IP addresses of each core and labeled egress router (LER) of the tree, our proposed scheme needs less memory space to maintain the code of the multicast tree.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".