A method generates a behavioral model of a data center when a machine learning algorithm is applied. A team of human modelers that partition the data center into a plurality of connected nodes is analyzed by a behavioral model. The behavioral model of the data center detects an anomaly in a system behavior center by recursively applying the behavioral model to each node and simple component. A compressed metric vector for the node is generated by reducing a dimension of an input metric vector. A root cause of a failure caused is determined by the anomaly and an action is automatically recommended to an operator to resolve a problem caused by the failure. The proactively actions are taken to keep the data center in a normal state based on the behavioral model using the machine learning algorithm.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of a server, comprising: generating a behavioral model of a data center when a machine learning algorithm is applied using a processor and a memory, wherein the behavioral model is structured based on analysis of a team of human modelers that partition the data center into a plurality of connected nodes, wherein each node is further decomposed by the team of human modelers into a connected set comprising at least one of a child node and a simple component, wherein the child node is the node which is a subset of another node, and wherein the simple component is the node which has not been further decomposed; detecting an anomaly in a system behavior using the behavioral model of the data center by recursively applying the behavioral model to each node and the simple component by, for each node: generating a compressed metric vector for the node by reducing a dimension of an input metric vector, wherein the input metric vector comprises at least one of a metric for the node and the compressed metric vector from the child node; determining whether anomalous behavior is occurring in the node by comparing the compressed metric vector with a compressed model vector, wherein the compressed model vector of the node is the compressed metric vector generated using at least one of the metric associated with the node operating in a satisfactory manner, and the compressed model vector of at least one child node; determining a root cause of a failure caused by the anomaly; automatically recommending an action to an operator to resolve a problem caused by the failure; and proactively taking actions to keep the data center in a normal state based on the behavioral model using the machine learning algorithm. 2. The method of claim 1: wherein the team of human modelers identify at least one characteristic comprising a label, a type, a category, and a connection of each of the nodes, and wherein the team of human modelers to manually define what constitutes a group each of the nodes having a similar characteristics in the server. 3. The method of claim 1: wherein the machine learning algorithm improves the behavioral model based on a human knowledge applied in real time as an input by the team of human modelers. 4. The method of claim 1: wherein the dimension of the input metric vector is reduced using at least one of a principal component analysis and a neural network. 5. The method of claim 4: wherein a full system model of the data center is automatically updated based on a dynamic change detected from at least one of a creation, a destruction, and a modification of at least one of an interconnection and a flow in the data center based on a reapplication of a human knowledge to further enhance the machine learning algorithm. 6. The method of claim 5: wherein the full system model of the data center is automatically updated based on the dynamic change detected when the node is at least one of added, deleted, and moved in the data center. 7. A method comprising: generating a behavioral model of a data center when a machine learning algorithm is applied using a processor and a memory, wherein the behavioral model is trained based on a human knowledge deconstruction of the data center into a set of connected simplified components; detecting an anomaly in a system behavior based on the behavioral model of the data center; proactively taking actions to keep the data center in a normal state based on the behavioral model using the machine learning algorithm. 8. The method of claim 7: determining a root cause of a failure caused by the anomaly; automatically recommending an action to an operator to resolve a problem caused by the failure, and wherein the behavioral model is generated based on analysis of a team of human modelers that decompose a complex system of the data center into a connected system of smaller constituent subsystems, and wherein a smaller constituent subsystems are further decomposed by the team of human modelers into a set of connected simple components. 9. The method of claim 8: wherein the team of human modelers identify at least one characteristic comprising a label, a type, a category, a connection, and a metric of each of the smaller constituent subsystems, and wherein a team of human modelers group each of the smaller constituent subsystems having a similar characteristics. 10. The method of claim 9: wherein the machine learning algorithm continually improves the behavioral model based on a human knowledge applied in real time as an input by the team of human modelers. 11. The method of claim 10: wherein the metric of each of the smaller constituent subsystems is compressed in a recursive fashion to ultimately build a full system model of the data center at a point in time. 12. The method of claim 11: wherein the full system model of the data center is automatically updated based on a dynamic change detected from at least one of a creation, a destruction, and a modification of at least one of an interconnection and a flow in the data center based on a reapplication of the human knowledge to further enhance the machine learning algorithm. 13. The method of claim 12: wherein the full system model of the data center is automatically updated based on the dynamic change detected when a component is at least one of added, deleted, and moved in the data center. 14. A system of an machine learning environment comprising: a computer server of the machine learning environment: the computer server including one or more computers having instructions stored thereon that when executed cause the one or more computers: to generate a behavioral model of a data center when a machine learning algorithm is applied using a processor and a memory, wherein the behavioral model is trained based on a human knowledge deconstruction of the data center into a set of connected simplified components; to detect an anomaly in a system behavior based on the behavioral model of the data center; to determine a root cause of a failure caused by the anomaly; to automatically recommend an action to an operator to resolve a problem caused by the failure; and to proactively take actions to keep the data center in a normal state based on the behavioral model using the machine learning algorithm. 15. The system of claim 14: wherein the behavioral model is generated based on analysis of a team of human modelers that decompose a complex system of the data center into a connected system of smaller constituent subsystems, and wherein a smaller constituent subsystems are further decomposed by the team of human modelers into a set of connected simple components. 16. The system of claim 15: wherein the team of human modelers identify at least one characteristic comprising a label, a type, a category, a connection, and a metric of each of the smaller constituent subsystems. 17. The system of claim 16 further comprising: wherein a team of human modelers group each of the smaller constituent subsystems having a similar characteristics. 18. The system of claim 17: wherein the machine learning algorithm continually improves the behavioral model based on a human knowledge applied in real time as an input by the team of human modelers. 19. The system of claim 18: wherein the metric of each of the smaller constituent subsystems is compressed in a recursive fashion to ultimately build a full system model of the data center at a point in time. 20. The system of claim 19: wherein the full system model of the data center is automatically updated based on a dynamic change detected from at least one of a creation, a destruction, and a modification of at least one of an interconnection and a flow in the data center based on a reapplication of the human knowledge to further enhance the machine learning algorithm.