论文标题
使用DBO的机器学习
Machine Learning with DBOS
论文作者
论文摘要
我们最近提出了一个以DBM为中心的新群集操作系统堆栈DBO。 DBO通过将ML代码封装在存储过程中，集中辅助ML数据，为基础DBMS内置的安全性，共同关注ML代码和数据，以及跟踪数据和工作流源出处，从而为ML应用程序提供了独特的支持。在这里，我们在两个ML应用程序附近演示了这些好处的子集。我们首先表明，使用GPU的图像分类和对象检测模型可以用作DBOS存储的过程，具有与现有系统竞争性能的DBO。然后，我们提出了一项1D CNN，该CNN训练有素，可以在DBOS支持的Web服务上检测HTTP请求中的异常情况，从而实现SOTA结果。我们使用该模型来开发交互式异常检测系统，并通过定性用户反馈对其进行评估，并证明了其有用性作为未来工作的概念证明，以在DBO上开发实时的实时安全服务。
We recently proposed a new cluster operating system stack, DBOS, centered on a DBMS. DBOS enables unique support for ML applications by encapsulating ML code within stored procedures, centralizing ancillary ML data, providing security built into the underlying DBMS, co-locating ML code and data, and tracking data and workflow provenance. Here we demonstrate a subset of these benefits around two ML applications. We first show that image classification and object detection models using GPUs can be served as DBOS stored procedures with performance competitive to existing systems. We then present a 1D CNN trained to detect anomalies in HTTP requests on DBOS-backed web services, achieving SOTA results. We use this model to develop an interactive anomaly detection system and evaluate it through qualitative user feedback, demonstrating its usefulness as a proof of concept for future work to develop learned real-time security services on top of DBOS.
