Learning-Assisted Write Latency Optimization for Mobile Storage

I/O activities of mobile storage are highly synchronous. Flash garbage collection activities in mobile storage introduce extra delay to write requests and negatively impact on user perceived-latency. Runtime write demand is subject to correlation between multiple parameters, such as network connectivity, GPS coordinates, and current time. We propose predicting write demand with a learning algorithm, XGBoost, and conducting background, rate-based garbage collection to optimize write latency without premature, excessive flash erasure. Our method reduced the 99-th percentile write latency by 56% compared to on-demand garbage collection and decreased flash erase count by 51% compared to unconditional background garbage collection.