RLAlloc: A Deep Reinforcement Learning-Assisted Resource Allocation Framework for Enhanced Both I/O Throughput and QoS Performance of Multi-Streamed SSDs
DescriptionMulti-streamed Solid-State Disks (SSDs) have attracted increasing adoption in modern flash storage devices. Despite their excellent promise, effective flash resource allocation is still limiting both their achievable I/O performance and practical implementation. To this end, we develop the first-of-its-kind framework dubbed RLAlloc, which for the first time demonstrates deep Reinforcement Learning-assisted resource Allocation for boosting both I/O throughput and QoS performance of multi-streamed SSDs. Extensive experiments consistently validate the effectiveness of RLAlloc, improving up to 39.9% on I/O throughput and 44.0% on QoS performance over the state-of-the-art competitors. All codes and training datasets will be released upon acceptance.
Event Type
Research Manuscript
TimeThursday, July 13th4:10pm - 4:25pm PDT
Location3006, 3rd Floor
Embedded Systems
Embedded Software