메뉴 건너뛰기
.. 내서재 .. 알림
소속 기관/학교 인증
인증하면 논문, 학술자료 등을  무료로 열람할 수 있어요.
한국대학교, 누리자동차, 시립도서관 등 나의 기관을 확인해보세요
(국내 대학 90% 이상 구독 중)
로그인 회원가입 고객센터 ENG
주제분류
인문학
인문학 일반 역사학 철학 종교학 어문학 기타 인문학
사회과학
사회과학 일반 경영학 관광학 교육학 군사학 문헌정보학 법학 사회학 신문방송학 심리과학 정치외교학 지리학 행정학 경제학 무역학 인류학 기타 사회과학
자연과학
자연과학 일반 물리학 생물학 생활과학 수학·통계학 천문학 지구과학 화학 기타 자연과학
공학
공학 일반 건축공학 기계공학 산업공학 재료·에너지공학 전기전자공학 조선해양공학 화학공학 컴퓨터학 기타공학
의약학
의학 일반 간호학 수의학 약학 한의학 기초의학 임상의학 기타 의약학
농수해양학
농수해양학 일반 농학 수산학 식품과학 임학 축산학 해상운송학 기타 농수해양학
예술체육학
예술 일반 디자인 미술 미용 사진 음악학 의상 체육 공연매체예술 기타 예술체육학
복합학
학제간연구 과학기술학 뇌인지과학 기술정책 기타 복합학

ENG

추천
검색
질문

논문 기본 정보

자료유형
학술저널
저자정보
Haotong Zhang (Chongqing Jiaotong University) Gang Xian (China Aerodynamics Research and Development Center) Wenxiang Yang (China Aerodynamics Research and Development Center) Jie Yu (China Aerodynamics Research and Development Center)
저널정보
Korean Institute of Information Scientists and Engineers Journal of Computing Science and Engineering Journal of Computing Science and Engineering Vol.16 No.4
발행연도
2022.12
수록면
222 - 232 (11page)
DOI
10.5626/JCSE.2022.16.4.222

이용수

표지
📌
연구주제
📖
연구배경
🔬
연구방법
🏆
연구결과
AI에게 요청하기
추천
검색
질문

초록· 키워드

오류제보하기
The powerful computing capabilities of supercomputers play an important role in today’s scientific computing. A large number of high-performance computing jobs are submitted and executed concurrently in the system. Job failure can cause a waste of system resources and impact the efficiency of the system and user jobs. Job failure prediction can support fault-tolerant technology to alleviate this phenomenon in supercomputers. At present, the related work mainly predicts job failure by collecting the real-time performance attributes of jobs, but it is difficult to apply in the real environment because of the high cost of collecting job attributes. In addition to analyzing the time and resource attributes in the job logs, this study also explores the semantic information of jobs. We mine job application semantic information from job names and job paths, where job path is collected by additional monitoring of the job submitting process. A prediction method based on job application semantic enhancement is proposed, and the prediction results of the non-ensemble learning algorithm and the ensemble learning algorithm are compared under each evaluation indicator. This prediction method requires more miniature feature collection and computation overhead and is easy to apply. The experimental results showed that the prediction effect was promisingly improved with job application semantic enhancement, and the final evaluation indicator S_score was improved by 5%–6%, of which was 88.16% accuracy with 95.23% specificity and 88.24% sensitivity.
#Execution efficiency #Job failure prediction #Application semantic enhancement #Machine learning

목차

Abstract
I. INTRODUCTION
II. RELATED WORK
III. BACKGROUND
IV. JOB LOGS ANALYSIS
V. SEMANTIC ENHANCEMENT
VI. EXPERIMENTATION AND RESULTS
VII. DISCUSSION
VIII. CONCLUSION
REFERENCES

참고문헌 (26)

참고문헌 신청
P. Uthayopas, T. Angskun, and J. Maneesilp, “Building a parallel computer from cheap PCs: SMILE cluster experiences,” in Proceedings of the 2nd Annual National Symposium on Computational Science and Engineering, Bangkok, Thailand, 1998. google schola X. J. Yang, X. K. Liao, K. Lu, Q. F. Hu, J. Q. Song, and J. S. Su, “The TianHe-1A supercomputer: its hardware and software,” Journal of Computer Science and Technology, vol. 26, no. 3, pp. 344-351, 2011. google schola J. Kunkel and M. F. Dolz, “Understanding hardware and software metrics with respect to power consumption,” Sustainable Computing: Informatics and Systems, vol. 17, pp. 43-54, 2018. google schola M. Soualhia, F. Khomh, and S. Tahar, “Predicting scheduling failures in the cloud: a case study with Google Clusters and Hadoop on Amazon EMR,” in Proceedings of 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, New York, NY, 2015, pp. 58-65. google schola D. Grzonka, A. Jakobik, J. Kołodziej, and S. Pllana, “Using a multi-agent system and artificial intelligence for monitoring and improving the cloud performance and security,” Future Generation Computer Systems, vol. 86, pp. 1106-1117, 2018. google schola

함께 읽어보면 좋을 논문

논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!

이 논문의 저자 정보

최근 본 자료

전체보기

댓글(0)

0