Outreach: peer-to-peer topology construction towards minimized server bandwidth costs
Bibliographic record
Abstract
On-demand and live multimedia streaming applications (such as Internet TV) are well known to utilize a significant amount of bandwidth from media streaming servers, especially as the number of participating peers in the streaming session scales up. To scale to higher bit rates of media streams and larger numbers of participating peers, overlay tree or mesh topologies are typically constructed, such that peers utilize their available upload capacities to alleviate the excessive bandwidth demands on stream servers. Previous works rely on random selections of upstream peers, without optimizing the topologies towards maximized utilization of peer upload bandwidth, and as a result, minimized bandwidth costs on streaming servers. We propose Outreach, a distributed algorithm to construct overlay topologies among participating peers in streaming sessions. The design objective of Outreach is to optimize the quality of overlay topologies towards scalability, with respect to the number of participating peers in the session. To be scalable, Outreach seeks to maximize the utilization of available upload bandwidth on each participating peer, and consequently minimize the total bandwidth costs on streaming servers. With analysis, we show that Outreach constructs topologies such that peers can fully utilize their upload capacities, and present a practical distributed algorithm. With simulation-based comparison studies, we show that Outreach effectively achieves its goals in a high-churn peer-to-peer network with an assortment of peer uplink capacities and link delays.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".