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An Approach for Detecting Abnormal Parallel Applications Based on Time Series Analysis Methods
5/5 checkable references clean · checked 2026-07-22
Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.
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The 5 checked references that resolve
resolves10.1007/978-3-319-49956-7_24
System Monitoring-Based Holistic Resource Utilization Analysis for Every User of a Large HPC Center
resolves10.1007/978-3-319-55669-7_27
Using Machine Learning Methods to Detect Applications with Abnormal Efficiency
resolves10.1080/01621459.2013.849605
A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data
The 5 references without a DOI — listed, not checked
no DOI — not checkedNikitenko, D.A., Voevodin Vad, V., Zhumatiy, S.A., Stefanov, K.S., Teplov, A.M., Shvets, P.A.: Supercomputer application integral characteristics analysis for the whole queued job collection of large-scale HPC systems. In: 10th Annual International Scientific Conference on Parallel Computing Technologies, Arkhangelsk, Russian Federation, CEUR Workshop Proceedings, vol. 1576, pp. 20–30 (2016)
no DOI — not checkedPedregosa, F., et al.: Scikit-learn: machine learning in python. JMLR 12, 2825–2830 (2011)
no DOI — not checkedCheboli, D.: Anomaly detection of time series. Dissertation, University of Minnesota (2010)
no DOI — not checkedMalhotra, P., Vig, L., Shroff, G., Agarwal, P.: Long short term memory networks for anomaly detection in time series. In: European Symposium on Artificial Neural Networks, vol. 23 (2015)
no DOI — not checkedVostrikova, L.: Detection disorder in multidimensional random processes. Sov. Math. Dokl. 24, 5559 (1981)
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