Balancing Power Grids and Maximizing Revenue: A Novel Approach to Rebate Auctions for Cloud Workload Migrations
Bibliographic record
Abstract
Revenue optimization is a main consideration in auction design. While it is a first-order objective in most auction settings, that is not the case for rebate auctions that use monetary rewards to incentivize auction participants to perform a task, where revenue optimization is secondary to successful task completion. This paper considers the case of VCG-based rebate auctions used to incentivize cloud workload migrations between datacenters to correct power-grid energy imbalances, and proposes a revenue maximization approach that takes into account the task completion objective (i.e., power-grid balancing) before a specified deadline. The proposed approach uses a task-completion constraint in the rebate auction optimization problem to ensure that the required task is completed on time, and uses predictions for the trend of future bid valuations (assumed to be gathered via a separate prediction module) to adjust the task-completion constraint to maximize revenue. Existing VCG-based revenue maximization approaches are not suitable for rebate auctions since they do not consider task completion deadlines (as regular auctions are not associated with tasks), and assume that bid valuations are randomly drawn from a probability distribution, which is not the case in rebate auctions. The proposed approach is compared against the existing rebate auction implementation (that does not consider the task completion deadline, nor the variations in bid valuations over time) in terms of its monetary effect on the auction participants and its ability to complete the required task on time. Simulation results show that the proposed approach improves the auctioneer’s revenue and consistently completes the energy-balancing task on time.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".