Online Anomaly Prediction for Real-Time Stream Processing

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概要

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• With the consideration of real-time stream processing technology, it's important to develop high availability mechanism to guarantee stream-based application not interfered by faults caused by potential anomalies. In this paper, we present a novel online prediction technique for predicting some anomalies which may occur in the near future. Concretely, we first present a value prediction which combines the Hidden Markov Model and the Mixture of Expert Model to predict the values of feature metrics in the near future. Then we employ the Support Vector Machine to do anomaly identification, which is a procedure to identify the kind of anomaly that we are about to alarm. The purpose of our approach is to achieve a tradeoff between fault penalty and resource cost. The experiment results show that our approach is of high accuracy for common anomaly prediction and low runtime overhead.

著者

• BAI Yuebin

  Sino-German Joint Software Institute, Beijing Key Laboratory of Network Technology, Beihang University

• HUANG Yuanqiang

  Sino-German Joint Software Institute, Beijing Key Laboratory of Network Technology, Beihang University

• LUAN Zhongzhi

  Sino-German Joint Software Institute, Beijing Key Laboratory of Network Technology, Beihang University

• QIAN Depei

  Sino-German Joint Software Institute, Beijing Key Laboratory of Network Technology, Beihang University

• DU Zhigao

  CNPC Research Institute of Safety and Environment Technology

• CHEN Ting

  Information and Communication Engineering, The University of Tokyo

関連論文

• Online Anomaly Prediction for Real-Time Stream Processing
• Online Anomaly Prediction for Real-Time Stream Processing

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