云成本优化团队终于有了一个靠谱的预测工具——在启动前就能看到跨区域最低价，省下 64% 的实例费用，做 AWS 基础设施的开发者建议直接看论文实现。
一篇新论文提出了一种在启动前预测 AWS Spot 舰队最低成本的方法。Spot 实例价格便宜但存在不确定性，用户通常无法在启动前跨区域比较最终价格。该研究构建了一个服务，通过监控 AWS 创建舰队的行为，利用时间感知 AI 模型学习模式，并估算 9 个区域的舰队组合和成本。用户只需输入计算需求和放置策略，服务就会返回按区域排序的选项。测试显示，预测准确率达 99.79%，选择最佳区域比改变区域内策略节省高达 64% 的成本。
This paper proposes a way to predict the cheapest …
This paper proposes a way to predict the cheapest safe AWS spot fleet before launching it.
AWS spot machines can be much cheaper, but users usually cannot see the final fleet price across regions before starting, so this paper turns that blind choice into a comparison that can save up to 64%.
Spot instances are cheap because they are conditional: the cloud provider can take them back, prices move, and capacity shifts by region.
The quiet problem is that AWS helps users launch spot fleets, but not fully see the fleet’s price or best region before launch.
The authors build a service that watches how AWS creates these fleets, learns those patterns with time-aware AI models, and then estimates the fleet mix and cost across 9 regions.
A user gives the service a target amount of computing power and a placement strategy, and the service returns region-ranked options before anything is launched.
They tested it on AWS with fleets up to 1500 virtual CPUs, using 720 test launches after a 90-day monitoring period.
The predicted fleet matched AWS exactly in 92.78% of cases, reached 99.79% overall accuracy against AWS behavior, and AWS accepted every recommended fleet.
Result is that choosing the best region mattered far more than changing the strategy inside 1 region, with possible savings up to 64%.
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Paper Link – arxiv. org/abs/2605.22778
Paper Title: "AI-Driven Multi-Region Provisioning for Cloud Services Using Spot Fleets"