Unsupervised behavior learning system and method for predicting performance anomalies in distributed computing infrastructures

A method of predicting performance anomalies in distributed computing infrastructure with a computer machine using unsupervised multi-dimensional behavior learning, the method comprising:

• aggregating and normalizing, with the computer machine, real-time data of a plurality of metrics for the distributed computing infrastructure;
• generating, with the computer machine, an artificial neural network model of normal and anomalous behavior for the distributed computing infrastructure based on unlabeled training data from a plurality of nodes associated with the distributed computing infrastructure, wherein the model includes a plurality of neurons;
• determining, with the computer machine, which neuron of the plurality of neurons an input real-time data should be mapped to and whether the input real-time data is normal or abnormal based on whether the mapped neuron represents a normal or abnormal system state;
• predicting, with the computer machine, a future system failure of the distributed computing infrastructure based on an evolution path in the artificial neural network model consisting of multiple consecutive comparisons of the real-time data to the artificial neural network model;
• upon predicting the future system failure, the computer machine generating a ranked list of metrics which are top contributors to the predicted future system failure of the distributed computing infrastructure, wherein the ranked list is generated based on differences between normal metric values and anomalous metric values
• selecting at least one preventative action based on the ranked list of metrics;
• and
• initiating the at least one preventative action.