Admission Control for Multimedia Delivery Over Deadline-Based Networks
Bibliographic record
Abstract
Increasing demand to transmit real-time data over packet-switched networks calls for quality-of-service support from the underlying network. Deadline-based networks were developed for this purpose. In a deadline-based network, each application data unit (ADU) is associated with a delivery deadline, it is specified by the sending application and represents the time at which the ADU should be delivered at the receiver. The ADU deadline is mapped to packet deadlines, which are carried by packets and used by routers for channel scheduling; deadline- based scheduling is employed in routers. It was shown that a deadline-based network provides better support to real-time data delivery than a first-come-first-served network. We study how to effectively and efficiently deliver multimedia data in deadline- based networks, especially when at heavy load. When network load is high, congestion may occur. Multimedia data may miss their delivery deadlines due to excessive queueing delays and high packet loss ratios. This would directly affect the playback quality at the application layer. To control the level of load and improve performance, two end-system based admission control algorithms are developed. Their performance is evaluated using simulation. Both schemes are shown to improve the performance of multimedia delivery over deadline-based networks.
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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 | 0.007 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".