Coordinate Live Streaming and Storage Sharing for Social Media Content Distribution
Bibliographic record
Abstract
The recently emerged user-generated contents (UGC) services, social networking services (SNS), as well as the pervasive wireless mobile network services have formed social media which has drastically changed the content distribution landscape. Today such UGC applications as YouTube allow any user to be a content provider, generating enormous amount of video contents that are quickly and extensively propagated on the Internet through such SNSes as Facebook and Twitter. Unfortunately, the existing UGC sites are facing critical server bottlenecks and the surges created by the social networking users would make the situation even worse. To better understand the challenges and opportunities therein, we investigate users' social behavior and personal preference of online video sharing from both real-trace measurement study on a popular social networking website and a user questionnaire survey. Our data analysis reveals an interesting coexistence of live streaming and storage sharing, and that the users are generally more interested in watching their friend's videos. It further suggests that even though the traffic is significant, most users are willing to share their resources to assist others, implying user collaboration is a rational choice in this context. In this paper, we present Coordinated Live Streaming and Storage Sharing (COOLS), a system for efficient peer-to-peer posting of user-generated videos. Through a novel ID code design that embeds nodes' locations in an overlay, COOLS leverages stable storage users and yet inherently prioritizes living streaming flows. We also present the improvement of the basic overlay design. The evaluation results show that, as compared to other state-of-the-art solutions, COOLS successfully takes advantage of the coexistence of live streaming and storage sharing, providing better scalability, robustness, and streaming quality.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".