Bibliographic record
Abstract
It is critical to monitor the performance and "health" of large-scale peer-to-peer applications. As an example, operators of peer-to-peer live streaming applications may be interested in observing performance bottlenecks, peer failures, and network topologies. In most cases, such observations are used to diagnose potential problems in the protocol design, to troubleshoot network outage, or to improve the Quality of Service of the peer-to-peer network in general. They are not time sensitive in nature, as delayed observations up to minutes or even hours are still valuable. However, such historical and delay-tolerant observations should include measurements of peers that have already failed or departed, as peer dynamics significantly affect the health of peer-to-peer applications. Such a delay-tolerant observation of peer-to-peer applications over a historical period of time is referred to as a diagnosis. In this paper, we present Echelon, a time-insensitive way to construct the diagnosis of a large-scale peer-to-peer application. Replacing the traditional wisdom of logging servers, we leverage the power of network coding to collect application-specific measurements on each peer, and disseminate them to other peers in a coded form. Over time, measurements of departed peers can still be recovered, simply by probing a small subset of peers in the network. Simulation studies have shown that Echelon is highly configurable, bandwidth efficient, and extremely tolerant of peer dynamics, thanks to the advantages of randomized network coding
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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 | 0.003 | 0.013 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".