Bibliographic record
Abstract
We present a study of pricing cloud resources in this position paper. Our objective is to explore and understand the interplay between economics and systems designs proposed by recent research. We develop a general model that captures the resource needs of various applications and usage pricing of cloud computing. We show that a uniform price does not suffer any revenue loss compared to first-order price discrimination. We then consider alternative strategies that a provider can use to improve revenue, including resource throttling and performance guarantees, enabled by recent technical developments. We prove that throttling achieves the maximum revenue at the expense of tenant surplus, while providing performance guarantees with an extra fee is a fairer solution for both parties. We further extend the model to incorporate the cost aspect of the problem, and the possibility of right-sizing capacity. We reveal another interesting insight that in some cases, instead of focusing on right-sizing, the provider should work on the demand and revenue side of the equation, and pricing is a more feasible and simpler solution. Our claims are evaluated through extensive trace-driven simulations with real-world workloads.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".