Predictable revenue under processor sharing
Bibliographic record
Abstract
This paper considers the case of a single service provider employing processor sharing discipline and serving randomly arriving users with random service requirements. The operator is assumed to charge a user based on the service rate allocated. The pricing mechanisms considered in this paper are the fixed rate pricing, Vickrey-Clarke-Groves (VCG) auctions, and congestion-based pricing (or the Lagrange shadow prices). Under such a model, we explicitly calculate the mean revenue of the operator and the mrean payments made by the users by exploiting the property of insensitivity associated with processor sharing. We also consider the effect of imposing a minimum rate requirement of a user on the revenue (a Quality of Service constraint). This paper presents our results and draws insights on the structure of the mean user payments and on the relation between the mean operator revenue and the zeroth, first, and the second moment of the total number of users present in the system under the three pricing mechanisms.
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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 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".