Optimal Prefetching Scheme in P2P VoD Applications With Guided Seeks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Most existing peer-to-peer (P2P) video-on-demand (VoD) systems have been designed and optimized for the sequential playback. In practice, users often want to seek to the positions they are interested in. Such frequent seeks raise greater challenges to the design of the prefetching scheme. In this work, we first propose the concept of guided seeks. With the guidance, users can perform more efficient seeks to the desired positions. The guidance can be obtained from collective seeking statistics of other peers who have watched the same title in the previous and/or concurrent sessions. However, it is very challenging to aggregate the statistics efficiently, timely and in a completely distributed way. We design the hybrid sketches that not only capture the seeking statistics at significantly reduced space and time complexity, but also adapt to the popularity of the video. From the collected seeking statistics, we estimate the segment access probability, based on which we further develop an optimal prefetching scheme and an optimal cache replacement policy to minimize the expected seeking delay at every viewing position. Through extensive simulations, we demonstrate that the proposed prefetching framework significantly reduces the seeking delay compared to the sequential prefetching scheme.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it