On Maximizing IP Multicast Throughput in Multi-Source Applications
Bibliographic record
Abstract
Given a fixed network of routers, a set of multicast sources and their corresponding receivers, we investigate the problem of constructing multicast sessions that maximize the multicast throughput of all sessions under fairness constraints. It is known that for problems with only one source node, heuristic algorithms based on packing maximum-rate Steiner trees may achieve throughput close to network capacity for some networks of interest. In almost all practical applications, however, multiple multicast sessions must be concurrently supported by the same network. We find that greedy strategies such as maximum-rate Steiner tree packing fail to perform well in dense problems, where the number of sources are large. We then propose a heuristic round-robin algorithm, called Cooperative Shortest Path Tree Packing Algorithm (CSPT), that performs uniformly well in the whole spectrum of problems from sparse to dense. Simulations on random networks show up to 5 times increase in throughput when compared to conventional methods in which there is only one tree per multicast session, and on average achieving 92% of the network capacity, when network coding is allowed. Finally, we show how CSPT can be implemented, with relative ease, on top of the current standard IP protocols.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".