International Business Machines Corporation

Marek Peszt

Piotr Padkowski

Bartlomiej Tomasz Malecki

Piotr Jozef Walczak

Krzysztof G Fabjanski

Wlodzimierz Martowicz

Piotr Kania

Agnieszka Tkaczyk

Agnieszka Tkaczyk-Walczak

Piotr Józef Walczak

Jagoda Kuzma

Embodiments of the present invention include a method for running a virtual manager scheduler for scheduling activities for virtual machines. The method may include: defining a schedule for one or more activities to be executed for a virtual machine; applying an adjustment to the schedule in accordance with feedback information received via a virtual machine client aggregating the feedback information from a plurality of virtual machine clients, each being related to a virtual machine, per scheduled activity type; and determining of a group adjustment for a determined group of the virtual machine clients based on a function of the feedback information of the plurality of virtual machine clients.

Feedback analysis for virtual machines manager scheduling

A method, a computer system, and a computer program product for clustering operational parameter values in a micro-service architecture used in a computing infrastructure. The computer system measures a plurality of operational parameter values of elements of the computing infrastructure and logs identifiers for elements having caused a problem situation and related problem resolution times. The computer system clusters the operational parameter values of the elements having caused the problem situation, according to a correlation function. The computer system orders the operational parameter values within a cluster and the elements having caused the problem situation. The computer system periodically performs the clustering and the ordering such that a sequence of the operational parameter values and the elements having caused the problem situation is indicative of a resolution time required for a new problem situation.

Optimized incident management using hierarchical clusters of metrics

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.

Distribution of software signatures using a community catalog based on blockchain

A method for connecting graph processing cut endpoints is disclosed. The method comprises determining a first capability matrix comprising capability values, applying weights to the capability values of the first capability matrix resulting in a first weighted capability matrix, and determining a second capability matrix comprising, for each pairing of another side of the cut endpoint, capability values. For both, the first capability matrix and second capability matrix, the capability values relate at least to a platform, a runtime framework, a connection technology, a protocol for a connection of a node of the stream processing system. The method comprises further determining a joint weighted capability matrix for two corresponding cut endpoints of a cut by multiplying corresponding capability matrix values of the first weighted capability matrix and the second capability matrix, and selecting a preferred connection for the cut endpoints.

Automatic selection of cut-point connections for dynamically-cut stream processing systems

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.

Detecting and predicting bottlenecks in complex systems

The invention relates to a sensor system. The sensor system comprises sensor devices. The sensor devices are configured for exchanging data via a network connection. Each of the sensor devices is configured to measure sensor data wherein each of the sensor devices comprises a local processor and a local memory coupled to the local processor. Each local memory comprises a local data repository. The local memory comprises local instructions which, when executed by the local processor, cause the local processor to: repeatedly store the sensor data in the local data repository as a data file, wherein each data file stored has a unique name; and repeatedly synchronize with the local data repository of the other of the sensor devices, wherein each local data repository maintains a complete copy of the data file of each of the sensor devices after synchronization.

Automated and distributed backup of sensor data

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.

Assigning a computer to a group of computers in a group infrastructure

A method for calculation of at least one software usage metric is disclosed, the software usage metric describing a plurality of software products installed in a computing infrastructure including a plurality of computing machines organized into a plurality of computing groups. The method includes installing a first set of software products in each computing machine in a first computing group of a plurality of computing groups, scanning one computing machine in the first computing group to discover that the first set of software products are installed thereon; and calculating an overall usage metric for the first computing group based on a number of computing machines belonging to the first computing group and the discovered first set of software products.

Calculation of a software usage metric

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.

Managing software asset environment using cognitive distributed cloud infrastructure

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.

Self-organizing software scan scheduling based on neural network cognitive classification

As disclosed herein a method, executed by a computer, includes generating, by a software asset management tool (SAM), a baseline event set comprising a plurality of events corresponding to an environment. The method further includes receiving a future event set comprising one or more future events corresponding to the environment. The method further includes combining the future event set and the baseline event set to produce an effective event set. The method further includes performing calculations and generating reports corresponding to the effective event set. A computer system, and a computer program product corresponding to the method are also disclosed herein.

Computing resource license planning

Embodiments of the present invention include a computer program product for running a virtual manager scheduler for scheduling activities for virtual machines. The computer program product may include program instructions to: define a schedule for one or more activities to be executed for a virtual machine; apply an adjustment to the schedule in accordance with feedback information received via a virtual machine client aggregating the feedback information from a plurality of virtual machine clients, each being related to a virtual machine, per scheduled activity type; and determine of a group adjustment for a determined group of the virtual machine clients based on a function of the feedback information of the plurality of virtual machine clients.

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Cracow, Poland

Marek Peszt