Measurement of the Effectiveness of Application-Layer Multicasting
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Multicasting is the data distribution from one sender to a group of receivers. Traditionally multicasting is implemented at network layer, in the way that routers perform membership management, maintain data delivery path, and replicate and forward data. IP Multicast is the most efficient way for group data distribution. However, it has been shown that it is extremely difficult to deploy IP Multicast at a large scale. Therefore an alternative has been proposed to shift multicast support to the application layer. This approach expects end-hosts participating in the application to perform multicast functions. This is application layer multicasting (ALM). This paper proposes a proxy-based single source ALM protocol which targets media streaming applications, where latency is the overlay building metric. A text-based message exchange application is implemented based on this protocol. Some measurements are taken both on the intranet and on the Internet. And some performance data are provided. Finally we concluded our research.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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 it