Design and performance of a scalable real time control protocol: simulations and evaluations
Bibliographic record
Abstract
Scalability problems which arise when the real time control protocol (RTCP) is used in large multicast groups include: increased feedback delay, increased storage state at every member; and ineffective RTCP bandwidth usage. This paper presents a scalable RTCP (S-RTCP). S-RTCP is based on a hierarchical structure in which members are grouped into local regions. For every region, there is an aggregator (AG) which receives the feedback receiver reports (RRs) of the local members, summarizes important information in the RRs, derives some statistics, and sends them to a manager. The manager performs additional statistical analysis to monitor the transmission quality and to identify regions of high congestion. A simulation of S-RTCP using the network simulator (NS) shows that S-RTCP alleviates some of RTCP scalability problems. Consequently, the feedback provides timely and useful QoS information required for network monitors and for adaptive applications. The main contribution of this paper is the presentation of the simulations' details and performance analysis that show the advantages of using S-RTCP over the original RTCP.
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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 | 0.002 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".