A performance modeling-based HADOOP configuration tuning strategy-Reference-Cited by-同舟云学术

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A performance modeling-based HADOOP configuration tuning strategy

Published:2022-02-01 Issue:3 Volume:7 Page:725-736
ISSN:2365-6379
Container-title:Nanotechnology for Environmental Engineering
language:en
Short-container-title:Nanotechnol. Environ. Eng.

Author:

Jie Huang

Funder

Changsha Aeronautical Vocational and Technical College Research Project in 2019

Publisher

Springer Science and Business Media LLC

Subject

Environmental Chemistry,Bioengineering,Environmental Engineering

Link

https://link.springer.com/content/pdf/10.1007/s41204-021-00184-3.pdf

Reference15 articles.

1. Mavridis I, Karatza H (2017) Performance evaluation of cloud-based log file analysis with apache Hadoop and apache spark. J Syst Softw 125:133–151. https://doi.org/10.1016/j.jss.2016.11.037

2. Wang M, Wu CQ, Cao H, Liu Y, Wang Y, Hou A. On mapreduce scheduling in Hadoop yarn on heterogeneous clusters. In: 2018 IEEE big data science and engineering, 2018, New York, NY, USA, 1–3 Aug 2018, pp 1747–1751. https://doi.org/10.1109/TrustCom/BigDataSE.2018.00264

3. Hua X, Huang MC, Liu P (2018) Hadoop configuration tuning with ensemble modelling and metaheuristic optimization. IEEE Access 6:44161–44174. https://doi.org/10.1109/ACCESS.2018.2857852

4. Cheng D, Zhou X, Xu Y, Liu L, Jiang C (2019) Deadline-aware MapReduce job scheduling with dynamic resource availability. IEEE Trans Parallel Distrib Syst 4(30):814–826. https://doi.org/10.1109/TPDS.2018.2873373

5. Jin P, Hao X, Wang X, Yue L (2019) Energy-efficient task scheduling for CPU-intensice streaming jobs on Hadoop. IEEE Trans Parallel Distrib Syst 6(30):1298–1311

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