QoS support of collaborative virtual environments applications in multiservice wireless networks through pricing
Bibliographic record
Abstract
In this paper we discuss the deployment of distributed interactive virtual environment (DIVE) applications over the wireless Internet. Our goal is to understand the behavior of a DIVE application, its interaction with competing traffic streams (video, data, voice, etc.), as well as its network resource requirements for a satisfactory performance in terms of quality of service (QoS). We manage QoS performance by introducing pricing principles for wireless channel resource allocation based on price "auctioning" or "bidding". The network controller advertises the available QoS levels and the mobile users are competing for them by placing bid requests. Based on variability of the wireless channel, the amount of available bandwidth shared between mobile users and the bid requests, the network controller exercises QoS management. The QoS levels assigned to every customer are dynamically changed depending on the network controller optimization criterion (in this paper the network controller revenue). A queuing theory model for QoS level determination based on bidding is presented, showing the ability of the pricing policy to provide the desired QoS for sensitive applications such as DIVE in a competitive environment.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".