Optimizing Cloud Pricing Strategies Using AWS Simulations and Dockerization
Bibliographic record
Abstract
Cloud computing is an essential element of modern software architecture, yet its pricing models often remain intricate and expensive. This paper proposes a cost-effective strategy for optimizing cloud expenditures using AWS simulations. Our method includes designing a system, preparing it for deployment, and utilizing Docker containers to reduce costs across various AWS services. This approach delivers substantial cost savings without sacrificing performance or scalability. Our results show that using AWS Fargate reduces monthly costs significantly compared to traditional virtual machines (EC2). Using identical parameters, an EC2 instance (t3.medium) cost $35.52 per month, while the ECS Fargate setup reduced that to $20.63. Fargate Spot Tasks further cut costs—up to 70% for non-critical workloads and around 40% for production deployments. Simulations with the AWS Pricing Calculator confirmed that Fargate simplifies infrastructure while enhancing scalability and security, all without server management overhead.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".