Machine Learning with DBOS
This work addresses the problem of efficiently deploying and securing ML applications in database-driven systems, though it is incremental as it builds on the existing DBOS framework.
The paper tackles the challenge of integrating machine learning applications with a database-centric operating system (DBOS) by encapsulating ML code in stored procedures and centralizing data, achieving competitive performance in image classification and object detection, and setting a state-of-the-art result for anomaly detection in HTTP requests.
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.