Pruning Deep Reinforcement Learning for Dual User Experience and Storage Lifetime Improvement on Mobile Devices

Abstract

Background segment cleaning in log-structured file system has a significant impact on mobile devices. A low triggering frequency of the cleaning activity cannot reclaim enough free space for subsequent I/O, thus incurring foreground segment cleaning and impacting the user experience. In contrast, a high triggering frequency could generate excessive block migrations (BMs) and impair the storage lifetime. Prior works address this issue either by performance-biased solutions or incurring excessive memory overhead. In this article, a pruned reinforcement learning-based approach, MOBC, is proposed. Through learning the behaviors of I/O workloads and the statuses of logical address space, MOBC adaptively reduces the number of BMs and the number of triggered foreground segment cleanings. In order to integrate MOBC to resource-constraint mobile devices, a structured pruning method is proposed to reduce the time and space cost. The experimental results show that the pruned MOBC can reduce the worst case latency by 32.5%-68.6% at the 99.9th percentile, and improve the storage endurance by 24.3% over existing approaches, with significantly reduced overheads.