Abstract: A computer-implemented method, and a computer system are provided for implementing dynamic and automatic altering a user profile based on machine learning and cognitive analysis to improve user performance. The user profile is dynamically altered based upon live data from multiple external data sources using machine learning and cognitive application programming interfaces (APIs) without explicit input from the user. The altered user profile is automatically stored for the user. The stored user profile is deployed for multiple selected user applications enabling enhanced performance for the user.

Abstract: Disclosed aspects include managing a set of wear-leveling data with respect to a set of physical cores of a set of compute nodes. A set of physical cores of the set of compute nodes may be monitored using a set of processor utilization resource registers (PURRs) to identify the set of wear-leveling data. By monitoring the set of physical cores of the set of compute nodes, a set of thread events with respect to the set of physical cores of the set of compute nodes may be detected. Based on the set of thread events, the set of wear-leveling data may be determined. The set of wear-leveling data may then be established in a data store. The wear leveling data may be used to manage asset placement with respect to a shared pool of configurable computing resources.

Abstract: Method and apparatus for resource pooling. Embodiments receive allocation information for a plurality of containers. Each of the plurality of containers is assigned to one of a plurality of groups. The allocation information specifies, for each of the plurality of groups, a respective number of physical CPUs to allocate to the group. A plurality of virtual resource pools are crated based on the allocation information wherein a single virtual resource pool is created for each of the plurality of groups. Embodiments create a container resource group mapping based on the allocation information. The container resource group mapping is a mapping between one or more physical CPUs in a shared processor pool and the plurality of virtual resource pools. Embodiments provide resources from the one or more physical CPUs to the plurality of containers according to the container resource group table and the virtual resource pools.

Abstract: Embodiments include method, systems and computer program products for protecting against unintentional command re-execution. The method includes applying a shell wrapper to a shell history in which the shell history includes one or more commands. The method further includes analyzing each of the one or more commands in the shell history using the shell wrapper. The method further includes disabling an ability to re-execute one or more commands in the shell history based on a determination that the one or more commands in the shell history are determined to be potentially dangerous.

Abstract: A method, system and computer program product for implementing paging device selection based on wear-level factor data in a computer system. Paging is used to store the temporary memory pages in a virtualization environment. Wear-level factor data is identified for each paging device. Selection of a paging device is based on the identified wear-level factor data.

Abstract: Embodiments include methods, systems, and computer program products for determining health care vital data. Aspects include receiving a health care vital measurement for a patient and health care vital data for a population. Aspects also include determining a baseline for the patient based at least in part upon the health care vital data for the population. Aspects include determining whether the patient health care vital data deviates from the baseline by more than a threshold and, applying a cognitive learning model to the patient health care vital measurement to correct for an anxiety-based impact to generate a corrected health care vital measurement responsive to a determination that the patient health care vital data deviates from the baseline by more than the threshold.

Abstract: Embodiments include methods, systems, and computer program products for determining health care vital data. Aspects include receiving a health care vital measurement for a patient and health care vital data for a population. Aspects also include determining a baseline for the patient based at least in part upon the health care vital data for the population. Aspects include determining whether the patient health care vital data deviates from the baseline by more than a threshold and, applying a cognitive learning model to the patient health care vital measurement to correct for an anxiety-based impact to generate a corrected health care vital measurement responsive to a determination that the patient health care vital data deviates from the baseline by more than the threshold.

Abstract: A computer-implemented method, and a computer system are provided for implementing dynamic and automatic altering a user profile based on machine learning and cognitive analysis to improve user performance. The user profile is dynamically altered based upon live data from multiple external data sources using machine learning and cognitive application programming interfaces (APIs) without explicit input from the user. The altered user profile is automatically stored for the user. The stored user profile is deployed for multiple selected user applications enabling enhanced performance for the user.

Abstract: A computer-implemented method, and a computer system are provided for implementing dynamic and automatic altering a user profile based on machine learning and cognitive analysis to improve user performance. The user profile is dynamically altered based upon live data from multiple external data sources using machine learning and cognitive application programming interfaces (APIs) without explicit input from the user. The altered user profile is automatically stored for the user. The stored user profile is deployed for multiple selected user applications enabling enhanced performance for the user.

Abstract: A system may be configured to provide secure access to a physical resource through the use of a partitionable virtual input/output server in a virtualized environment. A server may receive a request to access the physical resources from a cloud tenant administrator. The cloud tenant may be assigned to a particular working load partition (WPAR), and the physical resources may be assigned to the particular WPAR. A remote Kerberos server may be accessed in response to the request. The Kerberos server may be hosted in a private domain, and it may be used to authenticate access to the physical resource. The server may receive a valid ticket from the Kerberos server. The administrator may be granted access to the physical resources via access to the WPAR, based on the valid ticket and for the lifetime of the ticket.

Abstract: Embodiments include systems, methods, and computer program products to perform an operation for managing system activities in a cloud computing environment. The operation generally includes determining a type of system activity to perform on one or more servers in the cloud computing environment. The operation also includes identifying a set of locking parameters available for restricting system activity on the one or more servers. Each locking parameter corresponds to a different type of system activity. The operation further includes determining whether to perform the type of system activity based on a value of a locking parameter of the set of locking parameters associated with the type of system activity.

Abstract: Method and apparatus for resource pooling. Embodiments receive allocation information for a plurality of containers. Each of the plurality of containers is assigned to one of a plurality of groups. The allocation information specifies, for each of the plurality of groups, a respective number of physical CPUs to allocate to the group. A plurality of virtual resource pools are crated based on the allocation information wherein a single virtual resource pool is created for each of the plurality of groups. Embodiments create a container resource group mapping based on the allocation information. The container resource group mapping is a mapping between one or more physical CPUs in a shared processor pool and the plurality of virtual resource pools. Embodiments provide resources from the one or more physical CPUs to the plurality of containers according to the container resource group table and the virtual resource pools.

Abstract: Embodiments include method, systems and computer program products for protecting against unintentional command re-execution. The method includes applying a shell wrapper to a shell history in which the shell history includes one or more commands. The method further includes analyzing each of the one or more commands in the shell history using the shell wrapper. The method further includes disabling an ability to re-execute one or more commands in the shell history based on a determination that the one or more commands in the shell history are determined to be potentially dangerous.

Abstract: Embodiments describing an approach to creating, a user profile and linking, a mobile device to one or more mobile fitness devices. Continuously collecting, user fitness data from the one of more mobile fitness devices, and continuously collecting user data. Analyzing, the user fitness data and the user data, and Responsive to the user fitness data and user data analysis, generating a personalized nutrition plan based on the optimum parameters.

Abstract: Embodiments describing an approach to creating, a user profile and linking, a mobile device to one or more mobile fitness devices. Continuously collecting, user fitness data from the one of more mobile fitness devices, and continuously collecting user data. Analyzing, the user fitness data and the user data, and Responsive to the user fitness data and user data analysis, generating a personalized nutrition plan based on the optimum parameters.

Abstract: Embodiments include systems, methods, and computer program products to perform an operation for managing system activities in a cloud computing environment. The operation generally includes determining a type of system activity to perform on one or more servers in the cloud computing environment. The operation also includes identifying a set of locking parameters available for restricting system activity on the one or more servers. Each locking parameter corresponds to a different type of system activity. The operation further includes determining whether to perform the type of system activity based on a value of a locking parameter of the set of locking parameters associated with the type of system activity.

Abstract: Disclosed aspects include managing a set of wear-leveling data with respect to a set of physical cores of a set of compute nodes. A set of physical cores of the set of compute nodes may be monitored using a set of processor utilization resource registers (PURRs) to identify the set of wear-leveling data. By monitoring the set of physical cores of the set of compute nodes, a set of thread events with respect to the set of physical cores of the set of compute nodes may be detected. Based on the set of thread events, the set of wear-leveling data may be determined. The set of wear-leveling data may then be established in a data store. The wear leveling data may be used to manage asset placement with respect to a shared pool of configurable computing resources.

Abstract: Disclosed aspects include managing a set of wear-leveling data for a set of compute nodes. A set of bus traffic data may be monitored with respect to a bus which is connected to a computer hardware component of the set of compute nodes. In response to monitoring the set of bus traffic, the set of wear-leveling data may be determined using the set of bus traffic. The wear-leveling data determined using the set of bus traffic may then be established in a data store. The wear leveling data may be used to manage asset placement with respect to a shared pool of configurable computing resources.

Abstract: A method, system and computer program product for implementing paging device selection based on wear-level factor data in a computer system. Paging is used to store the temporary memory pages in a virtualization environment. Wear-level factor data is identified for each paging device. Selection of a paging device is based on the identified wear-level factor data.

Abstract: Disclosed aspects include managing a set of wear-leveling data with respect to a set of physical cores of a set of compute nodes. A set of physical cores of the set of compute nodes may be monitored using a set of processor utilization resource registers (PURRs) to identify the set of wear-leveling data. By monitoring the set of physical cores of the set of compute nodes, a set of thread events with respect to the set of physical cores of the set of compute nodes may be detected. Based on the set of thread events, the set of wear-leveling data may be determined. The set of wear-leveling data may then be established in a data store. The wear leveling data may be used to manage asset placement with respect to a shared pool of configurable computing resources.