ADSTS: Automatic Distributed Storage Tuning System Using Deep Reinforcement Learning-Reference-Cited by-同舟云学术

ADSTS: Automatic Distributed Storage Tuning System Using Deep Reinforcement Learning

Published:2022-08-29 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 51st International Conference on Parallel Processing
language:
Short-container-title:

Author:

Lu Kai¹,Li Guokuan¹,Wan Jiguang¹,Ma Ruixiang²,Zhao Wei²

Affiliation:

1. Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, China

2. SenseTime Research, China

Funder

the Creative Research Group Project of NSFC

the Key Research and Development Program of Guangdong Provinc

the National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3545008.3545012

Reference36 articles.

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2. Joe Arnold . 2014. Openstack swift: Using, administering, and developing for swift object storage . ” O’Reilly Media , Inc .”. Joe Arnold. 2014. Openstack swift: Using, administering, and developing for swift object storage. ” O’Reilly Media, Inc.”.

3. Taming parallel I/O complexity with auto-tuning

4. Zhen Cao , Geoff Kuenning , and Erez Zadok . 2020 . Carver: Finding important parameters for storage system tuning. In 18th {USENIX} Conference on File and Storage Technologies ({FAST} 20). 43–57. Zhen Cao, Geoff Kuenning, and Erez Zadok. 2020. Carver: Finding important parameters for storage system tuning. In 18th {USENIX} Conference on File and Storage Technologies ({FAST} 20). 43–57.

5. Zhen Cao Vasily Tarasov Sachin Tiwari and Erez Zadok. 2018. Towards better understanding of black-box auto-tuning: A comparative analysis for storage systems. In 2018 {USENIX} Annual Technical Conference ({USENIX}{ATC} 18). 893–907. Zhen Cao Vasily Tarasov Sachin Tiwari and Erez Zadok. 2018. Towards better understanding of black-box auto-tuning: A comparative analysis for storage systems. In 2018 {USENIX} Annual Technical Conference ({USENIX}{ATC} 18). 893–907.