ATC/OSDI 2023 BoF FlashNet

In the ML/Deep Learning community, the large ImageNet benchmarks have spurred research in image recognition. Similarly, we would like to provide benchmarks for fostering storage research in ML-based per-IO latency prediction. Therefore, we present FlashNet, a reproducible data science platform for storage systems. To start a big task, we use I/O latency prediction as a case study. Thus, FlashNet has been built for I/O latency prediction tasks. With FlashNet, data engineers can collect the IO traces of various devices. The data scientists then can train the ML models to predict the IO latency based on those traces. All traces, results, and codes will be shared in the FlashNet training ground platform which utilizes Chameleon trovi for better reproducibility.

Authors

Launch on Chameleon

Launching this artifact will open it within Chameleon’s shared Jupyter experiment environment, which is accessible to all Chameleon users with an active allocation.

Download Archive

Download an archive containing the files of this artifact.

Version Stats

8 4 2