Optimal Bandwidth Allocation for Dynamically Priced Network Services
Bibliographic record
Abstract
We have proposed the use of dynamically priced network services to provide QoS guarantees within a network. End-to-end QoS can be achieved by using several of these services, perhaps from different ISPs. In this paper we consider the problem of determining the bandwidth to allocate each service in order to maximize revenue, assuming that a single ISP can estimate the demand curves for each of its services. We develop and analyze two heuristics which provide time versus revenue tradeoffs. To determine the optimality of our solutions, we map the optimal allocation problem into a multiple choice multidimensional knapsack problem that approaches optimality as we increase the number of bandwidth allocation choices for each service. Our first heuristic, IterLP, achieves revenue close to 99% of the optimal solution, achieving this result in a very short time. The second heuristic, IterGreedy, achieves approximately 93% optimality, but executes more quickly than IterLP.
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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 | 0.000 |
| 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".