Truthful Online Auction Toward Maximized Instance Utilization in the Cloud
Bibliographic record
Abstract
Although infrastructure as a service (IaaS) users are busy scaling up/out their cloud instances to meet the ever-increasing demands, the dynamics of their demands, as well as the coarse-grained billing options offered by leading cloud providers, have led to substantial instance underutilization in both temporal and spatial domains. This paper systematically examines an instance subletting service, where sublettable instances can be leased to others within predetermined periods when underutilized, from both theoretical and practical perspectives. The studied instance subletting service extends and complements the existing instance market of IaaS providers. We identify the unique challenges and opportunities in this new service, and design online auction mechanisms to make allocation and pricing decisions for the instances to be sublet. For static supplies of instances, our mechanism guarantees truthfulness and individual rationality with the best possible competitive ratio. We then incorporate a multi-stage discount strategy to gracefully handle dynamic supplies. Extensive trace-driven simulations show that our service achieves significant performance gains in both cost savings and social welfare. We further pinpoint the challenges in implementing such a service in the real-world system and validate our modeling assumptions through a container-based prototype implemented over Amazon EC2.
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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 | 0.006 | 0.018 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".