Rohan Paul@rohanpaul_ai · X该研究提出了一种AI驱动的服务，用于在启动前预测最便宜且安全的AWS Spot实例舰队。该服务通过时间感知模型学习AWS创建舰队的模式，并估算9个区域的舰队组合与成本，向用户返回排序后的区域选项。测试显示，在最多1500 vCPU的舰队上，预测结果与AWS完全匹配的比例达92.78%，整体准确率为99.79%，且所有推荐舰队均被AWS接受。关键发现是选择最佳区域比在单个区域内调整策略更重要，潜在成本节省最高可达64%。
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.