To Reserve or Not to Reserve: Optimal Online Multi-Instance Acquisition\n in IaaS Clouds
Bibliographic record
Abstract
Infrastructure-as-a-Service (IaaS) clouds offer diverse instance purchasing\noptions. A user can either run instances on demand and pay only for what it\nuses, or it can prepay to reserve instances for a long period, during which a\nusage discount is entitled. An important problem facing a user is how these two\ninstance options can be dynamically combined to serve time-varying demands at\nminimum cost. Existing strategies in the literature, however, require either\nexact knowledge or the distribution of demands in the long-term future, which\nsignificantly limits their use in practice. Unlike existing works, we propose\ntwo practical online algorithms, one deterministic and another randomized, that\ndynamically combine the two instance options online without any knowledge of\nthe future. We show that the proposed deterministic (resp., randomized)\nalgorithm incurs no more than 2-alpha (resp., e/(e-1+alpha)) times the minimum\ncost obtained by an optimal offline algorithm that knows the exact future a\npriori, where alpha is the entitled discount after reservation. Our online\nalgorithms achieve the best possible competitive ratios in both the\ndeterministic and randomized cases, and can be easily extended to cases when\nshort-term predictions are reliable. Simulations driven by a large volume of\nreal-world traces show that significant cost savings can be achieved with\nprevalent IaaS prices.\n
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.008 | 0.020 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".