FS2You: Peer-Assisted Semipersistent Online Hosting at a Large Scale
Bibliographic record
Abstract
It has been widely acknowledged that online file hosting systems within the “cloud” of the Internet have provided valuable services to end users who wish to share files of any size. Such online hosting services are typically provided by dedicated servers, either in content distribution networks (CDNs) or large data centers. Server bandwidth costs, however, are prohibitive in these cases, especially when serving large volumes of files to a large number of users. Though it seems intuitive to take advantage of peer upload bandwidth to mitigate such server bandwidth costs in a complementary fashion, it is not trivial to design and fine-tune important aspects of such peer-assisted online hosting in a real-world large-scale deployment. This paper presents FS2You, a large-scale and real-world online file hosting system with peer assistance and semipersistent file availability. FS2You is designed to dramatically mitigate server bandwidth costs. In this paper, we show a number of key challenges involved in such a design objective, our architectural and protocol design in response to these challenges, as well as an extensive measurement study at a large scale to demonstrate the effectiveness of our design, using real-world traces that we have collected. To our knowledge, this paper represents the first attempt to design, implement, and evaluate a new peer-assisted semipersistent online file hosting system at a realistic scale. Since the launch of FS2You, it has quickly become one of the most popular online file hosting systems in mainland China, and a favorite in many online forums across the country.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".