RLAlloc: A Deep Reinforcement Learning-Assisted Resource Allocation Framework for Enhanced Both I/O Throughput and QoS Performance of Multi-Streamed SSDs-Reference-Cited by-同舟云学术

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RLAlloc: A Deep Reinforcement Learning-Assisted Resource Allocation Framework for Enhanced Both I/O Throughput and QoS Performance of Multi-Streamed SSDs

Published:2023-07-09 Issue: Volume: Page:
ISSN:
Container-title:2023 60th ACM/IEEE Design Automation Conference (DAC)
language:
Short-container-title:

Author:

Li Mengquan¹,Wu Chao²,Gao Congming³,Ji Cheng⁴,Li Kenli¹

Affiliation:

1. Hunan University,School of Computer Science and Electronic Engineering,CN

2. Northeastern University,Department of Electrical and Computer Engineering,USA

3. Xiameng University,School of Information,CN

4. Nanjing University of Technology,School of Computer Science and Engineering,CN

Funder

Research and Development

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10247654/10247655/10247988.pdf?arnumber=10247988

Reference23 articles.

1. Pcstream: automatic stream allocation using program contexts;kim;HotStorage Workshop,2018

2. Architecting Flash-based Solid-State Drive for High-performance I/O Virtualization

3. Intel ssd 5 series,0

4. Maximizing i/o throughput and minimizing performance variation via reinforcement learning based i/o merging for ssds;wu;IEEE TC,2019

5. AutoStream

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