Abstract: A method for event identification including receiving event information pertaining to events occurring with respect to a computing environment, each event having a measurement metric; evaluating by a probability function the measurement metric for each event to determine when the measurement metric is above a predetermined probability threshold or below a probability threshold wherein above a probability threshold or below a probability threshold is classified as alarm data; processing the alarm data through a decision tree to determine based on historical data when the alarm data is significant or when the alarm data is not significant and to reduce the number of alarm data to a predetermined number of significant alarm data; and displaying the predetermined number of significant alarm data to a user.

Abstract: Analyzing software change impact is provided. Feedback is received regarding a predicted impact of a software change to a set of components of a service. It is determined whether the predicted impact of the software change is correct based on the feedback. In response to determining that the predicted impact of the software change is correct based on the feedback, the software change is applied to the set of components of the service increasing performance of the service and a server computer hosting the service.

Abstract: Cognitively detecting cloud services and their associated status of a Virtual Machine and/or Container in a cloud platform to predict availability of cloud services preferably including the status of the services, a service object health map, and a service health status. The outputs are preferably sent to a user for feedback, which is sent back to the cognitive service.

Abstract: An approach is provided for orchestrating computing resources between different computing environments. Data from first and second computing environments is monitored. The data specifies utilization of infrastructure, middleware, software testing tools, integrated development environments (IDEs), relationships among nodes, utilization of the nodes, and user behavior in the first and second computing environments. Based on the utilization of the infrastructure, middleware, tools, IDEs, node relationships and utilization, and user behavior, a pattern decision tree is updated, and unbalanced workloads are identified. Based on a comparison of the unbalanced workloads to patterns in the updated pattern decision tree, an orchestration topology is generated that specifies a new placement of the computing resources in the first and second computing environments.

Abstract: Analyzing software change impact is provided. Feedback is received regarding a predicted impact of a software change to a set of components of a service. It is determined whether the predicted impact of the software change is correct based on the feedback. In response to determining that the predicted impact of the software change is correct based on the feedback, the software change is applied to the set of components of the service increasing performance of the service and a server computer hosting the service.

Abstract: Cognitively detecting cloud services and their associated status of a Virtual Machine and/or Container in a cloud platform to predict availability of cloud services preferably including the status of the services, a service object health map, and a service health status. The outputs are preferably sent to a user for feedback, which is sent back to the cognitive service.

Abstract: A method for event identification including receiving event information pertaining to events occurring with respect to a computing environment, each event having a measurement metric; evaluating by a probability function the measurement metric for each event to determine when the measurement metric is above a predetermined probability threshold or below a probability threshold wherein above a probability threshold or below a probability threshold is classified as alarm data; processing the alarm data through a decision tree to determine based on historical data when the alarm data is significant or when the alarm data is not significant and to reduce the number of alarm data to a predetermined number of significant alarm data; and displaying the predetermined number of significant alarm data to a user.

Abstract: An approach is provided for orchestrating computing resources between different computing environments. Data from first and second computing environments is monitored. The data specifies utilization of infrastructure, middleware, software testing tools, integrated development environments (IDEs), relationships among nodes, utilization of the nodes, and user behavior in the first and second computing environments. Based on the utilization of the infrastructure, middleware, tools, IDEs, node relationships and utilization, and user behavior, a pattern decision tree is updated, and unbalanced workloads are identified. Based on a comparison of the unbalanced workloads to patterns in the updated pattern decision tree, an orchestration topology is generated that specifies a new placement of the computing resources in the first and second computing environments.

Abstract: An approach is provided for orchestrating computing resources between different computing environments. Data from first and second computing environments is monitored. The data specifies utilization of infrastructure, middleware, software testing tools, integrated development environments (IDEs), relationships among nodes, utilization of the nodes, and user behavior in the first and second computing environments. Based on the utilization of the infrastructure, middleware, tools, IDEs, node relationships and utilization, and user behavior, a pattern decision tree is updated. Based on the updated pattern decision tree and a user request that specifies requirements of the computing resources, an orchestration topology is generated that specifies a placement of the computing resources in the first and second computing environments.

Abstract: An approach is provided for orchestrating computing resources between different computing environments. Data from first and second computing environments is monitored. The data specifies utilization of infrastructure, middleware, software testing tools, integrated development environments (IDEs), relationships among nodes, utilization of the nodes, and user behavior in the first and second computing environments. Based on the utilization of the infrastructure, middleware, tools, IDEs, node relationships and utilization, and user behavior, a pattern decision tree is updated. Based on the updated pattern decision tree and a user request that specifies requirements of the computing resources, an orchestration topology is generated that specifies a placement of the computing resources in the first and second computing environments.