Bibliographic record
Abstract
Peer-assisted Video-on-Demand (VoD) systems have not only received substantial recent research attention, but also been implemented and deployed with success in large-scale real- world streaming systems, such as PPLive. Peer-assisted Video- on-Demand systems are designed to take full advantage of peer upload bandwidth contributions with a cache on each peer. Since the size of such a cache on each peer is limited, it is imperative that an appropriate cache replacement algorithm is designed. There exists a tremendous level of flexibility in the design space of such cache replacement algorithms, including the simplest alternatives such as Least Recently Used (LRU). Which algorithm is the best to minimize server bandwidth costs, so that when peers need a media segment, it is most likely available from caches of other peers? Such a question, however, is arguably non-trivial to answer, as both the demand and supply of media segments are stochastic in nature. In this paper, we seek to construct an analytical framework based on optimal control theory and dynamic programming, to help us form an in-depth understanding of optimal strategies to design cache replacement algorithms. With such analytical insights, we have shown with extensive simulations that, the performance margin enjoyed by optimal strategies over the simplest algorithms is not substantial, when it comes to reducing server bandwidth costs. In most cases, the simplest choices are good enough as cache replacement algorithms in peer-assisted VoD systems.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 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".