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STATISTICALLY-BASED ANOMALY DETECTION IN UTILITY CLOUDS
STATISTICALLY-BASED ANOMALY DETECTION IN UTILITY CLOUDS
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机译：实用程序云中基于统计的异常检测
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摘要
Systems and methods for detecting anomalies in a large scale and cloud datacenter are disclosed. Anomaly detection is performed in an automated, statistical-based manner by using a parametric Gini coefficient technique or a non-parametric Tukey technique. In the parametric Gini coefficient technique, sample data is collected within a look-back window. The sample data is normalized to generate normalized data, which is binned into a plurality of bins defined by bin indices. A Gini coefficient and a threshold are calculated for the look-back window and the Gini coefficient is compared to the threshold to detect an anomaly in the sample data. In the non-parametric Tukey technique, collected sample data is divided into quartiles and compared to adjustable Tukey thresholds to detect anomalies in the sample data.
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