Bibliographic record
Abstract
Given a fixed network infrastructure, i.e. a set of multicast sources and their corresponding receivers, we investigate the problem of constructing multicast sessions that maximize network utilization for all sources involved, under a fairness constraint. This is done by ensuring that multicast session construction protocols uniformly distribute multicast traffic over all links. In most standard IP multicast protocols (e.g., PIM), a single multicast tree is constructed for each multicast session and all data packets corresponding to a session are multicast on the same tree. A key observation in this paper is that distributing multicast traffic for a session over multiple multicast trees can dramatically increase the load balance and improve network utilization. In fact, our simulations indicate that merely using a few multicast trees per session can improve the common throughput of all sessions by a factor of up to two. We devise a standard compliant, distributed protocol which we call load balanced and cooperative multicast or LBCM to efficiently construct multiple multicast trees for each multicast group. We show how LBCM may be implemented on top of standard multicast protocols to improve network utilization in currently deployed systems.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".