A peer-to-peer delivery system for internet short video sharing
Bibliographic record
Abstract
In this thesis, we considered the effect of the content delivery network architecture on the popular short video sharing websites such as YouTube. The high number of users demanding videos impacts YouTube scalability which requires a new content delivery structure. Considering the high performance of P2P overlay networks, we propose an efficient peer-to-peer based system for short video sharing in the Internet in which all participant peers are responsible to distribute video replicas they have stored. This system comprises of a BitTorrent like network and a central media streaming server. To proficiently utilize P2P in our system, we propose some important approaches including an efficient and reliable indexing scheme, an efficient downloading strategy, a reliable content distribution mechanism, and a fairness policy. The simulations results demonstrate that the proposed system significantly increases client peers download speed while reduces the server workload and the startup delay for an improved playback quality.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.009 | 0.027 |
| Research integrity | 0.001 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".