Prediction of job characteristics for intelligent resource allocation in HPC systems: a survey and future directions-Reference-Cited by-同舟云学术

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Prediction of job characteristics for intelligent resource allocation in HPC systems: a survey and future directions

Published:2022-05-23 Issue:5 Volume:16 Page:
ISSN:2095-2228
Container-title:Frontiers of Computer Science
language:en
Short-container-title:Front. Comput. Sci.

Author:

Hou Zhengxiong,Shen Hong,Zhou Xingshe,Gu Jianhua,Wang Yunlan,Zhao Tianhai

Publisher

Springer Science and Business Media LLC

Subject

General Computer Science,Theoretical Computer Science

Link

https://link.springer.com/content/pdf/10.1007/s11704-022-0625-8.pdf

Reference127 articles.

1. Feitelson D G, Tsafrir D, Krakov D. Experience with using the parallel workloads archive. Journal of Parallel and Distributed Computing, 2014, 74(10): 2967–2982

2. Wallace S, Yang X, Vishwanath V, Allcock W E, Coghlan S, Papka M E, Lan Z. A data driven scheduling approach for power management on HPC systems. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. 2016, 56

3. Tsujita Y, Uno A, Sekizaw R, Yamamoto K, Sueyasu F. Job classification through long-term log analysis towards power-aware HPC system operation. In: Proceedings of the 29th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP). 2021, 26–34

4. Fan Y, Rich P, Allcock W E, Papka M E, Lan Z. Trade-off between prediction accuracy and underestimation rate in job runtime estimates. In: Proceedings of the 2017 IEEE International Conference on Cluster Computing (CLUSTER). 2017, 530–540

5. Netto M A S, Calheiros R N, Rodrigues E R, Cunha R L F, Buyya R. HPC cloud for scientific and business applications: taxonomy, vision, and research challenges. ACM Computing Surveys, 2019, 51(1): 8

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