Anomaly detecting and ranking of the cloud computing platform by multi-view learning

  • Jing ZhangEmail author


Anomaly detecting as an important technical in cloud computing is applied to support smooth running of the cloud platform. Traditional detecting methods based on statistic, analysis, etc. lead to the high false-alarm rate due to non-adaptive and sensitive parameters setting. We presented an online model for anomaly detecting with the machine learning theory. However, most existing methods based on machine learning linked all features from difference sub-systems into a long feature vector directly, which is difficult to both exploit the complement information among sub-systems and ignore multi-view features enhancing the classification performance. Aiming to above problems, the proposed method automatic fuses multi-view features and optimizes the discriminative model to enhance the accuracy. This model takes advantage of extreme learning machine (ELM) to improve detection efficiency. ELM is the single hidden layer neural network, which is transforming iterative solution of the output weights to solution of linear equations and avoiding the local optimal solution. Moreover, we rank anomies according to the relationship between samples and the classification boundary, and then assigning weights for ranked anomalies, retraining the classification model finally. Our method exploits the complement information among sub-systems sufficiently, and avoids the influence from the imbalance distribution, therefore, deal with various challenges from the cloud computing platform. We deploy the privately cloud platform by Openstack, verifying the proposed model and comparing results to the state-of-the-art methods with better efficiency and simplicity.


Anomaly detection Cloud computing Extreme learning machine Multi-view fusing 



This work was supported by the National Natural Science Foundation of China under grants 61373127, 61772252, the Young Scientists Fund of the National Natural Science Foundation of China under grants 61702242 and the Doctoral Scientific Research Foundation of Liaoning Province under grants 20170520207.

The authors would like to thank the anonymous reviewers for the valuable suggestions they provided.


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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Computer and Information TechnologyLiaoning Normal UniversityDalianChina

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