Achieving optimal revenues in dynamically priced network services with QoS guarantees
Bibliographic record
Abstract
We have previously proposed the use of dynamically priced network services to provide QoS guarantees within a network. End-to-end QoS can be achieved by concatenating several of these services, perhaps from different ISPs. In this paper we consider the problem of a single ISP determining the bandwidth to allocate to each service, and on which path, in order to maximize revenue while guaranteeing end-to-end QoS. No knowledge of demand functions is assumed. Optimal allocation of bandwidth to services is first considered, where services are assumed to be routed on predetermined paths. We define the Iterative Allocation Adjustment heuristic, based on the concepts of tatonnement, which, through simulation, is shown to achieve over 95% of the optimal revenue for an ISP. We also examine how to value the links in the network to identify rerouting possibilities, or possible routes for new services, in order to improve the revenue of an ISP.
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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 | 0.006 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| 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".