Bibliographic record
Abstract
Telecommunication service providers are racing to deliver IPTV/video on demand (VoD), voice, and data, the so called triple-play services. IPTV traffic, supported by the UDP protocol, has highly variable data rates and stringent quality of services (QoS) requirements in terms of delay and loss. Data and VoD flows are normally supported by TCP, which has its own congestion control loop to adjust the sending rate, so the traffic load is also highly dynamic. If IPTV and TCP traffic is simply multiplexed, their performance is difficult to predict and the competition between them will jeopardize their QoS. To efficiently utilize network resources and provide satisfactory QoS for both traffic types, we propose multiplexing IPTV and TCP traffic with the protection of a class based queuing (CBQ) scheme. We also develop an analytical framework to model the multiplexed IPTV traffic and TCP traffic with CBQ. The analytical results can be used as a guide to determine the admission region of IPTV and the CBQ parameters. Simulation results are presented which validate the analytical results and demonstrate the effectiveness of the proposed solution. By multiplexing IPTV and TCP traffic appropriately, network resources can be more efficiently utilized, the QoS of IPTV can be maintained, and TCP flows can obtain higher throughputs.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.020 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".