Abstract: One or more computer processors responsive to a hypervisor data request, generate an encrypted set of hypervisor data indicating hypervisor hardware details using a plurality of respective asymmetric encryption keys to tag the generated set of encrypted hypervisor data with a timestamp at a time of encryption. The one or more computer processors decrypt the tagged set of encrypted hypervisor data utilizing a software asset manager and the plurality of respective asymmetric encryption keys determined valid based on a threshold time period and the tagged timestamp. The one or more computer processors validate the set of decrypted hypervisor data utilizing corresponding checksums to determine whether the set of encrypted hypervisor data was successfully decrypted. The one or more computer processors sanitize the validated hypervisor data based on a corresponding software license.

Abstract: One or more processors receive resource type and capability information and activity information of workloads of a domain. A first model is generated and trained to map the resource information to the activity information of domain workloads. The activity information is decomposed into a set of activity core elements (ACEs). The one or more processors generate a second model, wherein the second model is trained to predict a set of resource types and resource capabilities of the respective resource types, based on an input of the first set of ACEs decomposed from the activity information of the workloads of the domain. The one or more processors receive a second set of ACEs that are decomposed from activities associated with an unprecedented workload, and the one or more processors generate a predicted set of resources to perform the second set of ACEs.

Abstract: Providing a method for real-time testing and monitoring of a business process management system in order to identify commonly failing parameters. Machine learning methods are used to run multiple rounds of testing of the business process management system to more accurately identify commonly failing parameters and to modify the business process by removing those identified parameters from future rounds of testing.

Abstract: A computer implemented method and related system determine a current load result of a software container executing on a compute node in a container system. In response to determining that the current load result exceeds a predetermined scale-up threshold for the software container, the method adds a first plurality of replicas of the software container to the compute node, where a quantity of the first plurality of replicas is related to the current load result. In response to determining that the current load result is less than a predetermined scale-down threshold for the software container, the method deletes a second plurality of replicas of the software container from the compute node, where a quantity of the second plurality of replicas is related to the current load result.

Abstract: Provided are a computer program product, system, and method for scanning a computing system to determine a computing system profile and problems to recommend actions to initiate with the computing system. A package is transmitted to the computing system including package code to scan the computing system to determine a computing system profile comprising a computing architecture and installed applications at the computing system. The computing system profile is processed to determine a recommended action to perform with respect to the computing system to improve operations of the computing system based on the computing system profile. A display element is generated in a user interface with information on the recommended action to enable a user of the computing system to implement the recommended action. The package code executes within the computing system without communicating over the network to an external system outside of a computing environment of the computing system.

Abstract: A method is provided for rate limiting in a network. The method comprises receiving a traffic flow from the network. In a supervised learning phase, and determining if the traffic flow matches a pretrained network flow model. If so, the method comprises designating the traffic flow as a classified traffic flow according to the pretrained network flow model. The method further comprises advancing to a grouping phase, conditioned upon the traffic flow not matching pretrained network flow models. In the unsupervised learning phase, the method comprises designating the traffic flow as a classified traffic flow. In the grouping phase that follows the supervised learning phase and the unsupervised learning phase, the method comprises using side information about the traffic flows to assign related traffic flows into traffic flow groups, identifying a particular traffic flow group from the traffic flow groups as being an offending traffic flow group.

Abstract: A computer-implemented method obtains information about an external computer system having a connection to a computer system. The method may comprise receiving a request from the external computer system. In response to categorizing the external computer system as a potential threat responsive to the request, a computer file is generated responsive to the request. The computer file comprises instructions which are designed for obtaining the information about the external computer system if the instructions are executed on a processor of the external computer system. Furthermore, data is provided responsive to the request. The data is sent together with the computer file to the external computer system. The information about the external computer system, which is generated responsive to the instructions of the computer file, is received by the computer system for preserving it for a later use for developing measures for protecting the computer system.

Abstract: Provided are a computer program product, system, and method for scanning a computing system to determine a computing system profile and problems to recommend actions to initiate with the computing system. A package is transmitted to the computing system including package code to scan the computing system to determine a computing system profile comprising a computing architecture and installed applications at the computing system. The computing system profile is processed to determine a recommended action to perform with respect to the computing system to improve operations of the computing system based on the computing system profile. A display element is generated in a user interface with information on the recommended action to enable a user of the computing system to implement the recommended action. The package code executes within the computing system without communicating over the network to an external system outside of a computing environment of the computing system.

Abstract: A computer system provides dynamic support containers for containerized applications. A pod is instantiated comprising one or more containers and a sidecar container, wherein execution of the sidecar container is temporarily suspended after initialization. It is determined that a container of the one or more containers requires additional computing resources. In response to determining that the container requires additional computing resources, execution of the sidecar container is resumed and the sidecar container is provided with instructions to perform a computing task of the container. In response to determining that the computing task is complete, execution of the sidecar container is suspended. Embodiments of the present invention further include a method and program product for providing dynamic support containers for containerized applications in substantially the same manner described above.

Abstract: Technology for logging application usage based on monitoring of read or change operations on database elements and then using the information from log data to determine application usage states (for example, active, inactive) in time.

Abstract: An image for a containerized application is created. From the image, one or more main processes of the containerized application are identified. The one or more main processes comprise a first subset of a plurality of processes of the containerized application. Within a sandbox environment, the containerized application is started. Based on the started application within the sandbox environment, a second subset of processes within the plurality is determined. The second subset includes one or more dependent processes associated with the first subset. A third subset of processes within the plurality is determined. The third subset excludes the first and second subsets and is associated with a vulnerability list. According to the third subset, a mitigated image for the containerized application is generated.

Abstract: Aspects of the present invention disclose a method for deploying an application in a computing environment receiving an application module, determining values of a first set of metadata for the received application module, determining a classification of the received application module based at least in part on the values of the first set of metadata, and determining whether the received application is deployable in at least an off-premise system based at least in part on the classification.

Abstract: One or more computer processors install a trigger on a primary database. The one or more computer processors, responsive to the trigger activating and a data modification associated with the primary database, format the data modification into a universal format for a plurality of backup databases. The one or more computer processors rotate an active backup database from the plurality of backup databases based on a determined alternating backup period. The one or more computer processors synchronize in real-time the primary database with the active backup database.

Abstract: A computer system synchronizes data in an edge computing network. A leader node is elected from a plurality of nodes, wherein the plurality of nodes includes a plurality of follower nodes that each cast a single vote for a candidate node, and wherein the candidate node is elected as the leader node in response to the candidate node receiving votes from a majority of the nodes. The leader node receives a request from a follower node comprising data to be replicated across the nodes, and transmits the data to the other nodes. When a majority of nodes receive the data, the leader node transmits instructions to the nodes to cause each node to commit the data to a data log maintained by each node. Embodiments of the present invention further include a method and program product for synchronizing data in substantially the same manner described above.

Abstract: Aspects of the present invention disclose a method for deploying an application in a computing environment receiving an application module, determining values of a first set of metadata for the received application module, determining a classification of the received application module based at least in part on the values of the first set of metadata, and determining whether the received application is deployable in at least an off-premise system based at least in part on the classification.

Abstract: A first computer system is scanned at configured, default time intervals by a software asset management (“SAM”) software application installed on the first computer system, wherein the scanning detects other software applications installed on the first computer system. The SAM software application generates a list of the other software applications installed on the first computer system and stores the list on a computer readable storage medium. An override software module triggers the SAM software application to perform an early scan of the first computer system, before a scan of the first computer system that the SAM software application would otherwise perform at a next default scan time. The triggering is in response to information obtained from a source external to the first computer system about a triggering one of the software applications on the list.

Abstract: A computer implemented method and related system determine a current load result of a software container executing on a compute node in a container system. In response to determining that the current load result exceeds a predetermined scale-up threshold for the software container, the method adds a first plurality of replicas of the software container to the compute node, where a quantity of the first plurality of replicas is related to the current load result. In response to determining that the current load result is less than a predetermined scale-down threshold for the software container, the method deletes a second plurality of replicas of the software container from the compute node, where a quantity of the second plurality of replicas is related to the current load result.

Abstract: Provided are techniques for automated software application license usage. Global detection results that indicate, for each of a plurality of end user computers, one or more installed software applications, one or more running software applications, and a user account. Unique pairs are identified using the global detection results, wherein each pair of the unique pairs comprises a software application name for a software application and a user name for a user. For a unique pair, a query is run against a user registry to get a list of user groups that the user name is a member of, a user group is selected that corresponds to the software application name, and a number of users in the selected user group is designated as a license demand for authorized users for the software application. Then, based on the global detection results and the license demand, one or more recommendations are provided.

Abstract: One or more processors receive resource type and capability information and activity information of workloads of a domain. A first model is generated and trained to map the resource information to the activity information of domain workloads. The activity information is decomposed into a set of activity core elements (ACEs). The one or more processors generate a second model, wherein the second model is trained to predict a set of resource types and resource capabilities of the respective resource types, based on an input of the first set of ACEs decomposed from the activity information of the workloads of the domain. The one or more processors receive a second set of ACEs that are decomposed from activities associated with an unprecedented workload, and the one or more processors generate a predicted set of resources to perform the second set of ACEs.

Abstract: An image for a containerized application is created. From the image, one or more main processes of the containerized application are identified. The one or more main processes comprise a first subset of a plurality of processes of the containerized application. Within a sandbox environment, the containerized application is started. Based on the started application within the sandbox environment, a second subset of processes within the plurality is determined. The second subset includes one or more dependent processes associated with the first subset. A third subset of processes within the plurality is determined. The third subset excludes the first and second subsets and is associated with a vulnerability list. According to the third subset, a mitigated image for the containerized application is generated.