Restoring a system by load switching to an alternative cloud instance and self healing

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;
• generating a current state vector associated with a component of the one or more components based on the respective data feed relating to the component, wherein the current state vector represents a current performance of the component;
• comparing the current state vector associated with the component with a normal state vector associated with 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 feed of the component and the deviation of current state vector from the normal state vector, 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 the data feed received for the component wherein predicting the anomaly associated with the component comprises:
• generating the plurality of machine learning models, wherein each machine learning model uses a different algorithm to predict an anomaly associated with the component;
• predicting an anomaly using each of the plurality of machine learning models based at least on the data feed received from the component;
• comparing results from the predicting the anomaly using each of the plurality of machine learning models;
• selecting, based on the results, one of the machine learning models having a highest accuracy associated with the prediction of the anomaly;
• and
• selecting the anomaly predicted by the selected machine learning model as the predicted anomaly of the component;
• identifying a system configuration needed to run a current workload associated with the component, wherein the current workload includes processing at least one software application;
• searching each of a plurality of cloud infrastructures for a cloud instance that can support the identified system configuration, wherein each cloud infrastructure includes a set of one or more of hardware resources and software resources, wherein each cloud infrastructure provides one or more pre-configured cloud instances, wherein each cloud instance of a cloud infrastructure includes a unique set of hardware and software resources;
• identifying based on the search a cloud instance of a cloud infrastructure that can support the identified system configuration;
• initiating the identified cloud instance of the cloud infrastructure by creating in the cloud infrastructure a virtual machine corresponding to the identified cloud instance;
• and
• switching the current workload from an original system running the current workload to the initiated cloud instance.

A non-transitory computer-readable medium that stores instructions which when executed by a processor causes the processor to:

• receive 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;
• generate a current state vector associated with a component of the one or more components based on the respective data feed relating to the component, wherein the current state vector represents a current performance of the component;
• compare the current state vector associated with the component with a normal state vector associated with the component, wherein the normal state vector represents normal performance of the component;
• determine 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, predict an anomaly associated with the component using an iterative machine learning method based at least on the data feed of the component and the deviation of current state vector from the normal state vector, 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 the data feed received for the component wherein predicting the anomaly associated with the component comprises:
• generating the plurality of machine learning models, wherein each machine learning model uses a different algorithm to predict an anomaly associated with the component;
• predicting an anomaly using each of the plurality of machine learning models based at least on the data feed received from the component;
• comparing results from the predicting the anomaly using each of the plurality of machine learning models;
• selecting, based on the results, one of the machine learning models having a highest accuracy associated with the prediction of the anomaly;
• and
• selecting the anomaly predicted by the selected machine learning model as the predicted anomaly of the component;
• identify a system configuration needed to run a current workload associated with the component, wherein the current workload includes processing at least one software application;
• search each of a plurality of cloud infrastructures for a cloud instance that can support the identified system configuration, wherein each cloud infrastructure includes a set of one or more of hardware resources and software resources, wherein each cloud infrastructure provides one or more pre-configured cloud instances, wherein each cloud instance of a cloud infrastructure includes a unique set of hardware and software resources;
• identify based on the search a cloud instance of a cloud infrastructure that can support the identified system configuration;
• initiate the identified cloud instance of the cloud infrastructure by creating in the cloud infrastructure a virtual machine corresponding to the identified cloud instance;
• and
• switch the current workload from an original system running the current workload to the initiated cloud instance.