Large Scale Distributed Storage and Search for a Video on Demand Streaming System
Bibliographic record
Abstract
In the trend towards all IP networks, providing video services has proven to be a challenging task due to the high bandwidth requirements and the low delay and loss constraints. The architectural approaches taken by corporations to date mainly involve costly solutions that are difficult to manage and scale. Although IP multicast attempts to deal with the scalability of these systems at the network layer, it has not been widely deployed due to the extra cost of management and replacement of the existing infrastructure that involves. Therefore, if next generation service providers are to deliver high quality and on-demand video streaming to a large number of clients, new streaming methodologies such as peer-to-peer (P2P) need to be deployed to alleviate some of the current challenges involved in video streaming. We propose an effective and deployable solution for next generation video on demand (VoD) service providers based on a multilayered hybrid P2P topology for the distribution of and search for content, that addresses the requirements of low startup delay, provision of VCR-like commands and the scalability and management of a VoD streaming system.
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 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".