PreF: Predicting job failure on supercomputers with job path and user behavior-Reference-Cited by-同舟云学术

PreF: Predicting job failure on supercomputers with job path and user behavior

Published:2022-08-21 Issue:23 Volume:34 Page:
ISSN:1532-0626
Container-title:Concurrency and Computation: Practice and Experience
language:en
Short-container-title:Concurrency and Computation

Author:

Xian Gang¹²^ORCID,Zhang Xiaorong¹,Yu Jie²^ORCID,Wang Guijuan¹,Yang Wenxiang²³^ORCID,Zhou Longfang¹²,Wu Yadong⁴,Li Xuejun¹,He Xin⁴⁵

Affiliation:

1. School of Computer Science and Technology Southwest University of Science and Technology Mianyang China

2. Computational Aerodynamics Institute China Aerodynamics Research and Development Center Mianyang China

3. College of Computer National University of Defense Technology Changsha China

4. College of Mechanical and Electrical Engineering Chengdu University of Technology Chengdu China

5. School of Computer Science and Engineering Sichuan University of Science and Engineering Zigong China

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

Computational Theory and Mathematics,Computer Networks and Communications,Computer Science Applications,Theoretical Computer Science,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cpe.7202

Reference34 articles.

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2. Online diagnosis of performance variation in HPC systems using machine learning;Ozan T;IEEE Trans Parallel and Distrib Syst,2018

3. Using a multi-agent system and artificial intelligence for monitoring and improving the cloud performance and security

4. MbarkaS FoutseK SofieneT.Predicting scheduling failures in the cloud: A case study with google clusters and hadoop on amazon EMR. Proceedings of the IEEE 2015 IEEE 17th International Conference on High Performance Computing and Communications 2015 IEEE 7th International Symposium on Cyberspace Safety and Security and 2015 IEEE 12th International Conference on Embedded Software and Systems;2015:58‐65.

5. Bayes predictive analysis of a fundamental software reliability model

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