Abstract: A tool for network-based package monitoring and recovery. The tool scans a database to identify a gap in network traffic data. The tool determines whether at least one gap in the network traffic data is identified in the database, wherein the at least one gap is a gap in communication between a target device and a communication serve. Responsive to a determination that at least one gap in the network traffic data is identified, the tool determines a missing package from a package archive. The tool determines whether there is active network traffic between the target device and the communication server. Responsive to a determination that there is active network traffic between the target device and the communication server, the tool replays the missing package in the active network traffic between the target device and the communication server.

Abstract: In an approach for optimizing resources in a disaster recovery cleanup process, processors are configured for receiving transaction entries represented by transaction identifiers at a source database in communication with target databases via Synchronous-to-Asynchronous Traffic Converters (SATCs).

Abstract: Geographic location of a virtual server is predicted by determining a baseline geographic location of a virtual server of a computing environment, where the determining uses triangulation processing and known locations of multiple reference servers of the computing environment. Further, network communication-related data for communications between the multiple reference servers across a network is obtained, and a machine learning model is generated to predict an actual geographic location of the virtual server using, at least in part, the baseline geographic location of the virtual server and the obtained network communication-related data. The machine learning model is used to predict a current geographic location of the virtual server based, at least in part, on current network communication-related data for communications between the virtual server and one or more of the reference servers.

Abstract: An example operation may include one or more of generating a snapshot of a state of a permissioned blockchain, generating a unique identifier of the state snapshot that distinguishes the state snapshot from other state snapshots of the permissioned blockchain, storing the state snapshot in a data store, and storing proof of the state snapshot including the unique identifier and a storage location of the state snapshot on a public blockchain.

Abstract: Systems, methods, and computer programming products leveraging recurrent neural network architectures to proactively predict workload demand of container orchestration platforms. The platform continuously collects metric data from clusters of the platform and train multiple parallel neural networks with different architectures to predict future platform workload demands. At periodic intervals, the registered neural networks in consideration for controlling the scaling operations of the platform are compared against one another to identify the neural network demonstrating the highest performance and/or most accurate workload prediction strategy for scaling the orchestration platform. The selected neural network is enforced as controller for the platform to implement the workload prediction strategy.

Abstract: A backup and restore coordinator configured to receive a plurality of backup and restore requests from at least two uncoordinated backup functionalities implemented in a virtual environment, the virtual environment including a hypervisor hosting a plurality of virtual machines and a backup server. The backup and restore coordinator configured to extract respective information from the plurality of backup and restore requests including target data, backup resource information, and a type of request. The backup and restore coordinator configured to order the plurality of backup and restore requests in a prioritized queue based on the information extracted from the plurality of backup and restore requests.

Abstract: Generating a data protection and recovery data backup option by identifying static and runtime metadata for a computing application, determining application criticality of the computing application according to the static metadata using a first machine learning model, determining a data backup option for the computing application according to application criticality and the runtime metadata, using a second machine learning model.

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: According to one embodiment, a computer-implemented method for file handling in a hierarchical storage environment includes performing a file access notification process for determining files related to the first file based on enhanced metadata and a priority list defining a likelihood of possible access, in response to receiving a file access notification corresponding to access of a first file. The related files are placed in a highest level storage tier, and the priority list is updated.

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: In an approach for optimizing resources in a disaster recovery cleanup process, processors are configured for receiving transaction entries represented by transaction identifiers at a source database in communication with target databases via Synchronous-to-Asynchronous Traffic Converters (SATCs).

Abstract: Described are techniques for testing robotic process automation (RPA) bots. The techniques including a system comprising a computational environment configured to present a graphical user interface (GUI) on a display. The system further comprises an application configured to run on the computational environment. The system further comprises a robotic process automation (RPA) bot configured to execute tasks on the application using the GUI. The system further comprises an autonomous robot tester (ART) bot configured to execute a plurality of tests to disrupt the RPA bot by manipulating the GUI.

Abstract: A processor may provide a machine learning model. The machine learning model may have an input and an output. The processor may receive input data. The input data may include log data of a queried storage medium and a queried media drive. The processor may provide the input data to the input of the machine learning model. The processor may determine, from the output of the machine learning model, a predicted failure cause category and a predicted failure probability assigned to the predicted failure cause category. The processor may provide a first prediction to a user.

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: Generating a data protection and recovery data backup option by identifying static and runtime metadata for a computing application, determining application criticality of the computing application according to the static metadata using a first machine learning model, determining a data backup option for the computing application according to application criticality and the runtime metadata, using a second machine learning model.

Abstract: A method makes container services persistently available. A computing device receives a request for implementation of a user job in a container environment, and assigns the user job to a compute runner agent of a plurality of compute runner agents to execute the user job. Each compute runner agent is associated with a compute container instance having a unique compute container identifier corresponding to the user job. A computing device assigns the user job to a balancer task to monitor progress of the user job, and assigns the user job to a storage agent to store artifacts associated with running the user job. A computing device receives a notification from the balancer task describing whether the runner agent is correctly running the user job. In response to the runner agent incorrectly running the user job, a computing device resurrects the user job in a new compute container instance.

Abstract: A computer-implemented method according to one embodiment includes receiving a plurality of backup requests at a data storage system having a data storage library. At least some of the backup requests have unique service-metadata associated therewith. Each backup request is placed in a backup queue, where an ordering of the backup requests within the backup queue is based at least in part on the service-metadata. Backup operations are performed on the data storage system in accordance with the ordering of the backup requests within the backup queue. A computer program product for determining resource allocation in a backup environment according to another embodiment includes a computer readable storage medium having program instructions embodied therewith. The program instructions are readable and/or executable by a computer to cause the computer to perform the foregoing method.

Abstract: Methods, computer program products, devices, and/or systems are provided that perform the following operations: receiving data from an incoming data stream; separating the data into data chunks and storing the data chunks in accordance with a selection configuration matrix in a set of different matrix buffers until the matrix buffers are filled up; processing the data chunks by multiple processing units, each processing unit associated to a matrix buffer of the matrix buffers via an I/O block; providing the processed data chunks as inputs to a neural network component; and classifying the data and filtering the data based on a classification of the data by the neural network component.

Abstract: A method for linking a first knowledge graph (KG) and a second KG in the presence of a third KG is provided. Content of nodes of the first KG is compared to nodes of the second KG. If a first KG node has a content relationship to a related second KG node, an edge identified by a tuple identifying the first KG and the first KG node and a tuple identifying the second KG and the second KG node is stored in a meta-layer KG. The method comprises comparing content of the nodes from the third KG with the content of nodes from the first and second KG, and in case relationships are identified, more complex tuples establishing this relationship in the meta-layer are stored. Finally, the method also comprises storing at least all nodes and edges of the meta-layer knowledge graph.