Abstract: Methods, apparatus, and processor-readable storage media for correlating data center resources in a multi-tenant execution environment using machine learning techniques are provided herein. An example computer-implemented method includes obtaining multiple data streams pertaining to one or more data center resources in at least one multi-tenant executing environment; correlating one or more portions of the multiple data streams by processing at least a portion of the multiple data streams using at least one multi-tenant-capable search engine; determining one or more anomalies within the multiple data streams by processing the one or more correlated portions of the multiple data streams using a machine learning-based anomaly detection engine; and performing at least one automated action based at least in part on the one or more determined anomalies.

Abstract: Methods, apparatus, and processor-readable storage media for automated predictive infrastructure scaling are provided herein.

Abstract: A method, comprising: receiving, by an admission controller, a deployment object associated with code that is desired to be deployed to a production system; detecting, by a first responder, whether the deployment object is associated with a change ticket and, when the deployment object is not associated with a change ticket, submitting a change ticket that is associated with the deployment object, the change ticket being submitted by the first responder; detecting, by a second responder, whether the change ticket, that is associated with the deployment object, is allowed, and, when the change ticket is allowed, authorizing a deployment of the code, the deployment being authorized by the second responder; and deploying the code to a production system when the deployment of the code has been authorized by the second responder, the code being deployed by the admission controller.

Abstract: A method, comprising: receiving, by an admission controller, a deployment object associated with code that is desired to be deployed to a production system; detecting, by a first responder, whether the deployment object is associated with a change ticket and, when the deployment object is not associated with a change ticket, submitting a change ticket that is associated with the deployment object, the change ticket being submitted by the first responder; detecting, by a second responder, whether the change ticket, that is associated with the deployment object, is allowed, and, when the change ticket is allowed, authorizing a deployment of the code, the deployment being authorized by the second responder; and deploying the code to a production system when the deployment of the code has been authorized by the second responder, the code being deployed by the admission controller.

Abstract: Methods, apparatus, and processor-readable storage media for correlating data center resources in a multi-tenant execution environment using machine learning techniques are provided herein. An example computer-implemented method includes obtaining multiple data streams pertaining to one or more data center resources in at least one multi-tenant executing environment; correlating one or more portions of the multiple data streams by processing at least a portion of the multiple data streams using at least one multi-tenant-capable search engine; determining one or more anomalies within the multiple data streams by processing the one or more correlated portions of the multiple data streams using a machine learning-based anomaly detection engine; and performing at least one automated action based at least in part on the one or more determined anomalies.