The Benefits of Network Coding in Distributed Caching in Large-Scale P2P-VoD Systems
Bibliographic record
Abstract
Distributed caching mechanism plays an important role to improve the performance of large-scale peer-to-peer video-on-demand (P2P-VoD) systems, especially in terms of server bandwidth costs. Nevertheless, existing research and analytical studies of P2P-VoD systems have not thoroughly investigated and understood distributed caching policies and their critical properties for helping to mitigate the bandwidth costs on streaming servers. In particular, there exists no prior analytical work that focuses on a new way of designing a distributed caching strategy, with the help of network coding. In this paper, we seek to show an analytical understanding of the potential fundamental benefits of using network coding in distributed passive caching. With our problem formulation, we present probability-based expressions for computing the steady-state average server bandwidth costs, with or without the use of network coding. Our analytical results are cross-validated by our extensive simulation studies in large-scale static and dynamic scenarios.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".