Presentation
Black-box Auto-Tuning for Customized SSD Firmware Parameters under Constraints
DescriptionDatacenters and enterprise servers demand high-performance, customized SSDs optimized for specific I/O patterns to meet stringent Quality of Service requirements. We present an automated tuning framework leveraging Bayesian Optimization to effectively optimize firmware parameters for such customized SSDs. Our method was validated across multiple mass-produced SSD products, satisfying 94% of the targeted performance metrics and achieving an average latency reduction of 30.43 times compared to manual tuning. Furthermore, our distributed optimization system reduces tuning time from 19 days to 1.3 days by performing parallel evaluations, significantly enhancing development efficiency. This study is the first to apply Bayesian Optimization to SSD firmware optimization.
Event Type
Networking
Work-in-Progress Poster
TimeMonday, June 236:00pm - 7:00pm PDT
LocationLevel 2 Lobby