EVCCM: An Efficient VOIP Congestion Control Mechanism
Bibliographic record
Abstract
Voice over Internet protocol (VOIP) technology has been widely adopted in communication middleware systems that allow service providers to construct web based applications, such as Web conference systems (WebEx, Gotomeeting), IP based call centers and Web chatting. VOIP is vulnerable to network congestion if too many users access the Web service based VOIP at the same time, especially the services that coordinate with video transmission. VOIP signaling is usually implemented in User Datagram Protocol (UDP). Since UDP cannot verify the packets arrival, the congested network causes IP packets to be lost, delayed or even denial of service and greatly damages the service providers' reputation. We apply mechanism design, an application of game theory, to VOIP middleware management to defend such congested phone calls. Using the proposed model, users play their best options (ex: voice quality and price) to connect to the service. Service providers will provide users' service based on the users' options to maximize service providers' benefits, such as the number of active online users, service fee and system resource usage, to defend the congestion in the network and improve the network performance. In particular, our proposed model is a win-win solution in the way that maximizes both users and service providers' benefits. Finally, empirical results are provided to support our solution.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".