Improving Streaming Capacity in Multi-Channel P2P VoD Systems via Intra-Channel and Cross-Channel Resource Allocation
Bibliographic record
Abstract
Multi-channel Peer-to-Peer (P2P) Video-on-Demand (VoD) systems can be categorized intoindependent-channelP2P VoD systems andcorrelated-channelP2P VoD systems. Streaming capacity for a channel is defined as the maximal streaming rate that can be received by every user of the channel. In this paper, we study the streaming capacity problem in multi-channel P2P VoD systems. In an independent-channel P2P VoD system, there is no resource correlation among channels. Therefore, we can find the average streaming capacity for the independent-channel P2P VoD system by finding the streaming capacity for each individual channel, respectively. We propose a distributed algorithm to solve the streaming capacity problem for a single channel in an independent-channel P2P VoD system. The average streaming capacity for a correlated-channel P2P VoD system depends on both the intra-channel and cross-channel resource allocation. To better utilize the cross-channel resources, we first optimize the server upload allocation among channels to maximize the average streaming capacity and then propose cross-channel helpers to enable cross-channel sharing of peer upload bandwidths. We demonstrate in the simulations that the correlated-channel P2P VoD systems with both intra-channel and cross-channel resource allocation can obtain a higher average streaming capacity compared to the independent-channel P2P VoD systems with only intra-channel resource allocation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".