Machine Learning with DBOS

TL;DR

This paper demonstrates how the DBOS cluster operating system stack can effectively support machine learning applications by integrating ML models and workflows within a database-centric environment, enabling secure, efficient, and real-time ML services.

Contribution

It introduces the integration of ML workflows within DBOS, showcasing competitive performance for GPU-based models and a novel anomaly detection system with state-of-the-art results.

Findings

GPU-based image classification and object detection models perform competitively within DBOS.

A 1D CNN for HTTP request anomaly detection achieves state-of-the-art results.

The system enables real-time security services with positive user feedback.

Abstract

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.