Building Multicast Trees for Multimedia Streaming in Heterogeneous P2P Networks
Bibliographic record
Abstract
P2P networks have been proposed as a scalable, inexpensive solution to the problem of distributing multimedia content over the Internet. Since real P2P systems exhibit considerable heterogeneity in hardware, software and network connections, the design of P2P streaming networks must factor in this variation. There are two different sources of heterogeneity in P2P networks. Most existing work in the literature handle heterogeneity among receivers and requirements by the use of different multimedia encodings of the same content. In this paper we focus on the problems caused by heterogeneity in the network delays connecting receivers to the sender We assume that there is a single multicast tree and a single video stream. We propose new algorithms for building multicast trees for multimedia streaming in heterogeneous P2P networks. Our algorithms differ in the amount of communication and computational resources they require. We compare the performance (using simulations) of our algorithms with an existing Zigzag algorithm. Our results show that two of our algorithms ( FollowTree-Landmark-II algorithm and FollowTree algorithm) significantly outperform Zigzag.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".