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구분 SCI
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학술지 병렬 프로그램 로그 군집화 기반 작업 실행 시간 예측 모형 연구
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저자
김은혜, 박주원
발행일
201509
출처
산업경영시스템학회지, v.38 no.3, pp.56-63
ISSN
2005-0461
출판사
한국산업경영시스템학회
DOI
https://dx.doi.org/10.11627/jkise.2015.38.3.56
협약과제
15MC1100, SMART Post 구축 기술 개발, 정훈
초록
Several fields of science have demanded large-scale workflow support, which requires thousands of CPU cores or more. In order to support such large-scale scientific workflows, high capacity parallel systems such as supercomputers are widely used.In order to increase the utilization of these systems, most schedulers use backfilling policy: Small jobs are moved ahead to fill in holes in the schedule when large jobs do not delay. Since an estimate of the runtime is necessary for backfilling, most parallel systems use user’s estimated runtime. However, it is found to be extremely inaccurate because users overestimate their jobs. Therefore, in this paper, we propose a novel system for the runtime prediction based on workload-aware clustering with the goal of improving prediction performance. The proposed method for runtime prediction of parallel applications consists of three main phases. First, a feature selection based on factor analysis is performed to identify important input features. Then, it performs a clustering analysis of history data based on self-organizing map which is followed by hierarchical clustering for finding the clustering boundaries from the weight vectors. Finally, prediction models are constructed using support vector regression with the clustered workload data. Multiple prediction models for each clustered data pattern can reduce the error rate compared with a single model for the whole data pattern. In the experiments, we use workload logs on parallel systems (i.e., iPSC, LANL-CM5, SDSC-Par95, SDSC-Par96, and CTC-SP2) to evaluate the effectiveness of our approach. Comparing with other techniques, experimental results show that the proposed method improves the accuracy up to 69.08%.
KSP 제안 키워드
Clustered data, Clustering Analysis, Factor Analysis, Feature selection(FS), Hierarchical Clustering, High-capacity, History data, Input features, Parallel applications, Runtime Prediction, Scientific workflows