Name
Title | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TCAD.2020.3012804 | IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems |
Keywords | DocType | Volume |
Log-structured file system (LFS),mobile device,multiobjective deep reinforcement learning (RL),neuron network pruning,segment cleaning,storage lifetime,user experience | Journal | 39 |
Issue | ISSN | Citations |
11 | 0278-0070 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chao Wu | 1 | 15 | 8.86 |
| Yufei Cui | 2 | 6 | 7.02 |
| Cheng Ji | 3 | 10 | 3.93 |
| Tei-Wei Kuo | 4 | 3203 | 326.35 |
| Chun Jason Xue | 5 | 1616 | 140.95 |