WATuning: A Workload-Aware Tuning System with Attention-Based Deep Reinforcement Learning-Reference-Cited by-同舟云学术

WATuning: A Workload-Aware Tuning System with Attention-Based Deep Reinforcement Learning

Published:2021-07 Issue:4 Volume:36 Page:741-761
ISSN:1000-9000
Container-title:Journal of Computer Science and Technology
language:en
Short-container-title:J. Comput. Sci. Technol.

Author:

Ge Jia-Ke,Chai Yan-Feng,Chai Yun-Peng

Publisher

Springer Science and Business Media LLC

Subject

Computational Theory and Mathematics,Computer Science Applications,Hardware and Architecture,Theoretical Computer Science,Software

Link

https://link.springer.com/content/pdf/10.1007/s11390-021-1350-8.pdf

Reference33 articles.

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4. Chai Y P, Chai Y F, Wang X, Wei H C, Wang Y Y. Adaptive lower-level driven compaction to optimize LSM-Tree key-value stores. IEEE Transactions on Knowledge Data Engineering. https://doi.org/10.1109/TKDE.2020.3019264.

5. Zhu Y Q, Liu J X, Guo M Y, Bao Y G, Ma W L, Liu Z Y, Song K P, Yang Y C. BestConfig: Tapping the performance potential of systems via automatic configuration tuning. In Proc. ACM Symposium on Cloud Computing, Sept. 2017, pp.338-350. https://doi.org/10.1145/3127479.3128605.

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