Abstract: A mechanism is provided for adding software signatures to a software catalog may be provided. The software catalog comprises a vendor-maintained software catalog section, a user-maintained software catalog section, and a community-maintained software catalog section. The mechanism scans a system using a software asset management tool. The mechanism adds a new software signature to the community-maintained software catalog section via a Blockchain transaction. An acceptance of the signature into the community-maintained software catalog section is based on a received Blockchain proof-of-work message.

Abstract: A method for detecting a bottleneck in a system includes receiving a graph, wherein a node represents a software module and an edge represents a communication channel between software modules, monitoring selected resources for each software module in comparison to available resources, monitoring a ratio of a bandwidth consumed on a communication channel versus available bandwidth, traversing the graph for identifying a source software module whose produced amount of output is below the amount of output needed by the software module that is in idle mode, and analyzing a resource consumption of the identified source software module to identify a lacking amount of resource for the identified source software module. A computer system and computer program product corresponding to the above method are also disclosed herein.

Abstract: Assigning a given computer to a computer group of a set of computer groups. Software components installed on the given computer are scanned, resulting in a list of discovered software components of the given computer. In addition, for each computer group of the set of computer groups, a list of software components most frequently installed on the computers of the computer group is obtained. For each computer group, the list is compared with the list of discovered software components and, based on the comparison, a likelihood that the given computer belongs to the computer group is computed. In case only one of the likelihoods exceeds a threshold, the given computer is assigned to the respective computer group for which the likelihood exceeds the threshold.

Abstract: A method and system are provided for performing an extract-transform-load (ETL) process. The method includes collecting load information about a volume and a complexity of raw data to be processed during the ETL process. The method further includes receiving an expected completion time of the ETL process and execution information about (i) hardware resources and (ii) an influence of the hardware resource on an execution time of the ETL process. The method also includes calculating resources for a distributed processing software infrastructure to be used to perform the ETL process, by applying a statistical method to the load information, expected completion time, and execution information. The method additionally includes dynamically assigning cloud resources corresponding to and based on the calculated resources, in accordance with the expected completion time. The method further includes performing the ETL process on the raw data using the assigned cloud resources and storing ETL process results.

Abstract: An approach for updating of a repetition frequency of a system scan operation. The approach calculates values of asset management factors based on results of the asset management factors from a current and a previously performed system scan operation. Groups of the computer systems are treated as node equivalents, and the asset management factors are treated as synapse equivalents of the node equivalents. The approach also feeds values of the factors and weighing values as input for determining an update value for the repetition frequency as output. The weighing value is adaptable via the learning capability of the neural network equivalent. Finally, the repetition frequency is updated using the update value by an activation function.

Abstract: Assigning a given computer to a computer group of a set of computer groups. Software components installed on the given computer are scanned, resulting in a list of discovered software components of the given computer. In addition, for each computer group of the set of computer groups, a list of software components most frequently installed on the computers of the computer group is obtained. For each computer group, the list is compared with the list of discovered software components and, based on the comparison, a likelihood that the given computer belongs to the computer group is computed. In case only one of the likelihoods exceeds a threshold, the given computer is assigned to the respective computer group for which the likelihood exceeds the threshold.

Abstract: Assigning a given computer to a computer group of a set of computer groups. Software components installed on the given computer are scanned, resulting in a list of discovered software components of the given computer. In addition, for each computer group of the set of computer groups, a list of software components most frequently installed on the computers of the computer group is obtained. For each computer group, the list is compared with the list of discovered software components and, based on the comparison, a likelihood that the given computer belongs to the computer group is computed. In case only one of the likelihoods exceeds a threshold, the given computer is assigned to the respective computer group for which the likelihood exceeds the threshold.

Abstract: A mechanism is provided for adding software signatures to a software catalog may be provided. The software catalog comprises a vendor-maintained software catalog section, a user-maintained software catalog section, and a community-maintained software catalog section. The mechanism scans a system using a software asset management tool. The mechanism adds a new software signature to the community-maintained software catalog section via a Blockchain transaction. An acceptance of the signature into the community-maintained software catalog section is based on a received Blockchain proof-of-work message.

Abstract: A method and system are provided for performing an extract-transform-load (ETL) process. The method includes collecting load information about a volume and a complexity of raw data to be processed during the ETL process. The method further includes receiving an expected completion time of the ETL process and execution information about (i) hardware resources and (ii) an influence of the hardware resource on an execution time of the ETL process. The method also includes calculating resources for a distributed processing software infrastructure to be used to perform the ETL process, by applying a statistical method to the load information, expected completion time, and execution information. The method additionally includes dynamically assigning cloud resources corresponding to and based on the calculated resources, in accordance with the expected completion time. The method further includes performing the ETL process on the raw data using the assigned cloud resources and storing ETL process results.

Abstract: A method for detecting a bottleneck in a system includes receiving a graph, wherein a node represents a software module and an edge represents a communication channel between software modules, monitoring selected resources for each software module in comparison to available resources, monitoring a ratio of a bandwidth consumed on a communication channel versus available bandwidth, traversing the graph for identifying a source software module whose produced amount of output is below the amount of output needed by the software module that is in idle mode, and analyzing a resource consumption of the identified source software module to identify a lacking amount of resource for the identified source software module. A computer system and computer program product corresponding to the above method are also disclosed herein.

Abstract: An approach for updating of a repetition frequency of a system scan operation. The approach calculates values of asset management factors based on results of the asset management factors from a current and a previously performed system scan operation. Groups of the computer systems are treated as node equivalents, and the asset management factors are treated as synapse equivalents of the node equivalents. The approach also feeds values of the factors and weighing values as input for determining an update value for the repetition frequency as output. The weighing value is adaptable via the learning capability of the neural network equivalent. Finally, the repetition frequency is updated using the update value by an activation function.

Abstract: Assigning a given computer to a computer group of a set of computer groups. Software components installed on the given computer are scanned, resulting in a list of discovered software components of the given computer. In addition, for each computer group of the set of computer groups, a list of software components most frequently installed on the computers of the computer group is obtained. For each computer group, the list is compared with the list of discovered software components and, based on the comparison, a likelihood that the given computer belongs to the computer group is computed. In case only one of the likelihoods exceeds a threshold, the given computer is assigned to the respective computer group for which the likelihood exceeds the threshold.

Abstract: Managing an application zone is provided. A request is received from a program on a client device to enter a zone of an application that provides a service. In response to determining that the zone does not currently exist in the application, the zone is generated in the application based on defined parameters of the zone. An enter notification is sent to the program on the client device indicating that the zone is ready for the program to enter to receive the service.