Effective Utilization of User Resources in PA-VoD Systems with Channel Heterogeneity
Bibliographic record
Abstract
Nowadays, peer-assisted video on-demand (PA-VoD) systems offer high-definition (HD) channels to online users. However, the quality of service in such HD channels is usually not comparable to the standard-definition (SD) ones, as HD channels have to seek more bandwidth support and cache space from peers, which is a challenging task. In this paper, we focus on peer cache and upload bandwidth management at the same time for multi-channel PA-VoD systems with heterogeneous video playback rates, i.e., HD and SD channels coexist with different bandwidth and cache requirements. We first take user viewing behaviors into account and derive the statistical performance bounds on server bandwidth consumption, which lead to the conclusion that such behaviors can easily affect the provisioning for HD channels, even if there is enough upload bandwidth from SD peers. We then formulate bandwidth allocation as a linear programming problem to calculate the tight lower bound at any time instant, with global information available and system-wide coordination possible (e.g., through a tracker). Next, we design heuristic algorithms for peer cache replacement and upload bandwidth allocation to fit with the nature of a P2P structure, and the results are compared with the statistical and instance performance bounds through extensive simulation, which shows the efficacy of the proposed algorithms in 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 | 0.002 | 0.006 |
| 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 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".