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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".