Efficient allocation of seed servers in peer-to-peer streaming systems with scalable videos
Bibliographic record
Abstract
We study streaming of scalable videos over peer-to-peer (P2P) networks. We focus on efficient management of seed servers resources, which need to be deployed in the network to make up for the limited upload capacity of peers in order to deliver higher quality video streams. These servers have finite serving capacity and are often loaded with a volume of requests larger than their capacity. We formulate the problem of allocating this capacity for optimally serving scalable videos. We show that this problem is NP-complete, and propose two approximation algorithms to solve it. The first one allocates seeding resources for serving peers based on dynamic programming, and is more suitable for small seeding capacities (les 10 Mbps). The second algorithm follows a greedy approach and is more efficient for larger capacities. We evaluate the proposed algorithms analytically and in a simulated P2P streaming system. The results confirm the efficiency and near-optimality of the proposed algorithms, and show that higher-quality videos are delivered to peers if our algorithms are employed for allocating seed servers.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".