Optimal Prefetching Scheme in P2P VoD Applications With Guided Seeks
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.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".