  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 using 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 between sub-systems and ignore multi-view features
enhancing the classification performance. Aiming to this problem, the proposed
method automatic fuses multi-view features and optimize 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 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 between sub-systems sufficiently, and avoids the influence from
imbalance dataset, 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.
