Integrating Predictable Planning and Unpredictable Dynamics in Network Utility Maximization: An Improved Quota-Based Market Model
Bibliographic record
Abstract
Pricing schemes for network utility maximization are playing an increasingly important role for optimal resource allocation within communication networks. Nevertheless, most pricing-based resource allocation mechanisms in the literature are quite complicated and may not be practical to implement in real network. In this paper, we propose a novel incentive engineering mechanism which incorporates a cumulus point mechanism facilitating the mapping of user's traffic from shorter time scales to relative longer time scale, and at longer time scale, implements a virtual monetary nuglet mechanism derived from classic quota-based predictive scheme. The nuglets will be allocated to each user according to agreed traffic contract and will be traded between users and service provider for real transmission service. This improved quota-based market model facilitates both prediction of resource allocation for each user and adjustment of admission decision making based on network dynamics. The system model shows the application of proposed mechanism in admission control for a congested access point in wireless network. Through decomposing original problem into distributed optimization problems separately and solved by service provider locally through adjusting charging rate or individually at user's side by changing her service requests reasonably, maximization of both user's utility and service provider's revenue will be achieved. Evaluation demonstrates that our improved quota-based market model indeed provides a good balance among various perspectives that define the overall performance of a charging scheme.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".