RLAlloc: A Deep Reinforcement Learning-Assisted Resource Allocation Framework for Enhanced Both I/O Throughput and QoS Performance of Multi-Streamed SSDs.
DAC(2023)
Abstract
Multi-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.
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Key words
deep Reinforcement Learning-assisted resource Allocation framework,effective flash resource allocation,first-of-its-kind framework dubbed RLAlloc,modern flash storage devices,Multistreamed Solid-State Disks,multistreamed SSDs,QoS performance
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