Maximizing Cloud Revenue using Dynamic Pricing of Multiple Class Virtual Machines
Bibliographic record
Abstract
The Infrastructure as a Service (IaaS) cloud industry that relies on leasing virtual machines (VMs) has significant portion of business values of finding the dynamic equilibrium between two conflicting phenomena: underutilization and surging congestion. Spot instance has been proposed as an elegant solution to overcome these challenges, with the ultimate goal to achieve greater profits. However, previous studies on recent spot pricing schemes reveal artificial pricing policies that do not comply with the dynamic nature of these phenomena. Motivated by these facts, this paper investigates dynamic pricing of stagnant resources in order to maximize cloud revenue. Specifically, our proposed approach manages multiple classes of virtual machines in order to achieve the maximum expected revenue within a finite discrete time horizon. For this sake, the proposed approach leverages the Markov decision processes with a number of properties under optimum controlling conditions that characterize a model's behaviour. Further, this approach applies approximate stochastic dynamic programming using linear programming to create a practical model. Experimental results confirm that this approach of dynamic pricing can scale up or down the price efficiently and effectively, according to the stagnant resources and the load thresholds. These results provide significant insights to maximizing the IaaS cloud revenue.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".