Towards Foundation Database Models
This paper introduces the concept of "foundation models for databases," a new paradigm advocating for pre-trained, general-purpose models that can be adapted to various tasks and datasets with minimal overhead, moving away from the current inefficient one-off model approach.
Jan 20, 2025
Sleuth: A Trace-Based Root Cause Analysis System for Large-Scale Microservices with Graph Neural Networks
Sleuth is a root cause analysis system that uses unsupervised graph learning on trace data to accurately and adaptably identify performance bottlenecks in large-scale microservice applications.
Feb 7, 2024
Sage: Practical & Scalable ML-Driven Performance Debugging in Microservices
Sage is a machine learning-driven root cause analysis system that uses unsupervised models to accurately identify and correct performance violations in complex cloud microservices by analyzing their dependencies.
Apr 13, 2021