Automatic system anomaly detection

A method for managing anomalies in a production computing environment, comprising:

• receiving a data feed relating to one or more of a plurality of components of the production computing environment, wherein the data feed received for each component includes information relating to performance of the component, wherein the plurality of components includes at least one hardware component and at least one software component, wherein the at least one hardware component comprises a hardware device and the at least one software component comprises a software application;
• storing the data feed received for each component in a centralized data repository as a data log;
• for each component:
• generating a current state vector based on the respective data log relating to the component, wherein the current state vector represents a current performance of the component;
• comparing the current state vector of the component with a normal state vector of the component, wherein the normal state vector represents normal performance of the component;
• determining that the current state vector of the component deviates, at least in part, from the normal state vector of the component;
• in response to detecting the deviation, predicting an anomaly associated with the component using an iterative machine learning method based at least on the data log of the component and the determined deviation, wherein the iterative machine learning method uses a plurality of machine learning models to predict the anomaly and iteratively updates training of each of the machine learning models using at least the most current data feed received for the component;
• and
• correcting the anomaly by taking at least one pre-configured action corresponding to the predicted anomaly.

A computer-readable medium for managing anomalies in a production computing environment, wherein the computer-readable medium stores instructions which when processed by a processor performs a method comprising:

• receiving a data feed relating to one or more of a plurality of components of the production computing environment, wherein the data feed received for each component includes information relating to performance of the component, wherein the plurality of components includes at least one hardware component and at least one software component, wherein the at least one hardware component comprises a hardware device and the at least one software component comprises a software application;
• storing the data feed received for each component in a centralized data repository as a data log;
• for each component:
• generating a current state vector based on the respective data log relating to the component, wherein the current state vector represents a current performance of the component;
• comparing the current state vector of the component with a normal state vector of the component, wherein the normal state vector represents normal performance of the component;
• determining that the current state vector of the component deviates, at least in part, from the normal state vector of the component;
• in response to detecting the deviation, predicting an anomaly associated with the component using an iterative machine learning method based at least on the data log of the component and the determined deviation, wherein the iterative machine learning method uses a plurality of machine learning models to predict the anomaly and iteratively updates training of each of the machine learning models using at least the most current data feed received from the component;
• and
• correcting the anomaly by taking at least one pre-configured action corresponding to the predicted anomaly.