TurBO: A cost-efficient configuration-based auto-tuning approach for cluster-based big data frameworks-Reference-Cited by-同舟云学术

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TurBO: A cost-efficient configuration-based auto-tuning approach for cluster-based big data frameworks

Published:2023-07 Issue: Volume:177 Page:89-105
ISSN:0743-7315
Container-title:Journal of Parallel and Distributed Computing
language:en
Short-container-title:Journal of Parallel and Distributed Computing

Author:

Dou Hui^ORCID,Zhang Lei^ORCID,Zhang Yiwen^ORCID,Chen Pengfei^ORCID,Zheng Zibin

Funder

National Natural Science Foundation of China

Natural Science Foundation of Anhui Province

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Networks and Communications,Hardware and Architecture,Theoretical Computer Science,Software

Reference39 articles.

1. Cherrypick: adaptively unearthing the best cloud configurations for big data analytics;Alipourfard,2017

2. Opentuner: an extensible framework for program autotuning;Ansel,2014

3. Autoconfig: automatic configuration tuning for distributed message systems;Bao,2018

4. Actgan: automatic configuration tuning for software systems with generative adversarial networks;Bao,2019

5. Osc: an online self-configuring big data framework for optimization of qos (tc-2020-02-0128.r1);Bei;IEEE Trans. Comput.,2021

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. CTuner: Automatic NoSQL Database Tuning with Causal Reinforcement Learning;Proceedings of the 15th Asia-Pacific Symposium on Internetware;2024-07-24

2. Towards an Optimized Distributed Message Queue System for AIoT Edge Computing: A Reinforcement Learning Approach;Sensors;2023-06-08

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