Abstract: In some implementations, a data materialization platform may perform a data migration process between a core cloud environment and an edge cloud environment. The data materialization platform may identify, in association with the data migration process, attribute values stored in a data repository of the core cloud environment, wherein the attribute values are to be used as inputs for a machine learning model that is to be executed by the edge cloud environment. The data materialization platform may analyze the attribute values to identify ranges, associated with a subset of the attribute values, for which outputs of the machine learning model are estimated to be approximately a same output. The data materialization platform may deduplicating, by the data materialization platform, the subset of the attribute values from the data repository of the core cloud environment to generate deduplicated attribute values that are associated with median range values.

Abstract: A set of virtual machines is deployed on a hypervisor. At each virtual machine, one or more local scans is performed to generate a set of computing resource consumption data. In response to receiving a set of hypervisor resource consumption data, a stability factor is generated. Based on the set of resource consumption data, the set of hypervisor resource consumption data, and the stability factor, a determination is made that a default consumption metric exceeds a threshold. In response to the determination, a custom consumption metric is generated, based on at least the set of computing resource consumption data. A user is notified of the custom consumption metric.

Abstract: Resource load balancing for eNodeB includes identifying reduced workload conditions for an underutilized processor (CPU) core and transferring dedicated traffic channel (DTCH) communication to a target CPU core. Upon migrating each DTCH from the selected CPU core, the underutilized CPU core is caused to go into sleep mode, not to receive DTCH request until activated.

Abstract: A first backend housekeeping operation of a backend storage system is detected by a storage virtualization system. The first backend housekeeping operation is related to a first backend storage portion of the backend storage system. The storage virtualization system transfers data to the backend storage system for storage and retrieval. The storage virtualization system issues commands to the backend storage system. A first virtualized operation related to a first virtualized storage portion of the storage virtualization system is identified. A storage overhead condition is determined based on the first backend storage portion and based on the first virtualized operation. A storage corrective action is performed based on the storage overhead condition. The storage corrective action prevents the potential performance of one or more additional backend housekeeping operations.

Abstract: Resource load balancing for eNodeB includes identifying reduced workload conditions for an underutilized processor (CPU) core and transferring dedicated traffic channel (DTCH) communication to a target CPU core. Upon migrating each DTCH from the selected CPU core, the underutilized CPU core is caused to go into sleep mode, not to receive DTCH request until activated.

Abstract: A method, a computer system, and a computer program product for tiering migration for storage system optimization is provided. Embodiments of the present invention may include determining to move data based on access counters to balance a workload between storage drives and storage tiers. Embodiments of the present invention may include establishing a connection with an interconnect application program interface. Embodiments of the present invention may include collecting and converting physical access counters. Embodiments of the present invention may include collecting heat map data. Embodiments of the present invention may include calculating a tiering migration plan for an internal map change event.

Abstract: In an approach to improve the field of multi-cloud environments by detecting data corruption between storage systems. Embodiments perform information tunneling on data transferring between a source storage system and a target storage system. Further, embodiments determine a checksum data of a data payload does not match an Internet Protocol (IP) packet extracted checksum and a blockchain based checksum and compare the checksum data at the target storage system with the IP packet extracted checksum and the blockchain based checksum to identify one or more checksum mismatches. Additionally, embodiments identify a corruption in a data payload based on the comparison between the checksum data at the target storage system and the IP packet extracted checksum and the blockchain based checksum, validate the corruption in the data payload, and update respective entities of identified corruption in the data payload.

Abstract: When a system receives sensitive data, it can request an encryption key from an encryption/decryption unit. A central processing unit (CPU) of the system can encrypt the sensitive data using the encryption key before writing the sensitive data to memory. Thus, the sensitive data is encrypted when written to memory.

Abstract: A first backend housekeeping operation of a backend storage system is detected by a storage virtualization system. The first backend housekeeping operation is related to a first backend storage portion of the backend storage system. The storage virtualization system transfers data to the backend storage system for storage and retrieval. The storage virtualization system issues commands to the backend storage system. A first virtualized operation related to a first virtualized storage portion of the storage virtualization system is identified. A storage overhead condition is determined based on the first backend storage portion and based on the first virtualized operation. A storage corrective action is performed based on the storage overhead condition. The storage corrective action prevents the potential performance of one or more additional backend housekeeping operations.

Abstract: When a system receives sensitive data, it can request an encryption key from an encryption/decryption unit. A central processing unit (CPU) of the system can encrypt the sensitive data using the encryption key before writing the sensitive data to memory. Thus, the sensitive data is encrypted when written to memory.

Abstract: A method, system, and computer program product for managing input/output (I/O) operations in log structured arrays (LSA) is provided. The method receives a write request for a storage volume. The write request including a set of data. The method determines the set of data for a target extent of the storage volume is not copied to a target location based on a target map for a flash copy volume. An LSA is instructed to perform an internal copy-on-write (COW) for the set of data during a next IO operation for the storage volume. Based on the internal COW instruction, the method moves an internal pointer of the target location from a virtual volume to the flash copy volume.

Abstract: In an approach to improve the field of multi-cloud environments by detecting data corruption between storage systems. Embodiments perform information tunneling on data transferring between a source storage system and a target storage system. Further, embodiments determine a checksum data of a data payload does not match an Internet Protocol (IP) packet extracted checksum and a blockchain based checksum and compare the checksum data at the target storage system with the IP packet extracted checksum and the blockchain based checksum to identify one or more checksum mismatches. Additionally, embodiments identify a corruption in a data payload based on the comparison between the checksum data at the target storage system and the IP packet extracted checksum and the blockchain based checksum, validate the corruption in the data payload, and update respective entities of identified corruption in the data payload.

Abstract: A method, system and computer program product for light-weight software license compliance management. One embodiment of the method comprises accessing a set of software logs from a target device, analyzing the set of software logs to generate a license violation probability score, determining the license violation probability score satisfies a predetermined threshold, and notifying a user of a need to scan the target device for software license compliance.

Abstract: Disclosed is a system, computer program product and computer-implemented method for smog analysis which provides sending a data collection signal to one or more vehicles including one or more sensors via a base station, receiving data from the one or more vehicles, including sensor data, analyzing the received data for determining a smog status, and sending traffic information to a plurality of vehicles. The traffic information may include the determined smog status and the plurality of vehicles may include the one or more vehicles.

Abstract: A scanner is used to generate a set of processing power consumption metrics for a memory subsystem. From the set of processing power consumption metrics, a number of in-memory computations is determined. From the number of in-memory computations, a total computing score is determined. A user is notified of the total computing score.

Abstract: A set of virtual machines is deployed on a hypervisor. At each virtual machine, one or more local scans is performed to generate a set of computing resource consumption data. In response to receiving a set of hypervisor resource consumption data, a stability factor is generated. Based on the set of resource consumption data, the set of hypervisor resource consumption data, and the stability factor, a determination is made that a default consumption metric exceeds a threshold. In response to the determination, a custom consumption metric is generated, based on at least the set of computing resource consumption data. A user is notified of the custom consumption metric.

Abstract: Providing reproducible machine learning model results by receiving input data for a machine learning (ML) model, processing the input data using the ML model, yielding an initial result, adding a first block to a distributed ledger, the block comprising the input data, the initial result, an ML model data structure, and a link to training data for the ML model, wherein the training data resides in previous distributed ledger blocks, and providing an output including the initial result.

Abstract: A method, a computer system, and a computer program product for tiering migration for storage system optimization is provided. Embodiments of the present invention may include determining to move data based on access counters to balance a workload between storage drives and storage tiers. Embodiments of the present invention may include establishing a connection with an interconnect application program interface. Embodiments of the present invention may include collecting and converting physical access counters. Embodiments of the present invention may include collecting heat map data. Embodiments of the present invention may include calculating a tiering migration plan for an internal map change event.

Abstract: A method, system, and computer program product for managing input/output (I/O) operations in log structured arrays (LSA) is provided. The method receives a write request for a storage volume. The write request including a set of data. The method determines the set of data for a target extent of the storage volume is not copied to a target location based on a target map for a flash copy volume. An LSA is instructed to perform an internal copy-on-write (COW) for the set of data during a next IO operation for the storage volume. Based on the internal COW instruction, the method moves an internal pointer of the target location from a virtual volume to the flash copy volume.

Abstract: A computer-implemented method, system and computer program product for applying a unified governance and integration platform to social media data. Data integration definitions for managing and protecting social media data are received. After receiving the data integration definitions for social media data, data integration externalization, governance catalog externalization or lineage externalization may be performed. For example, social media data may be extracted from a database system by a governance and integration unit (“unit”) using the data integration definitions. The extracted social media data is then transformed and loaded to the social media data system via a connector linking the data from the unit to the social media data system. In another example, social media data is extracted from the social media data system by the unit, such as via the connector, using the data integration definitions and then transformed and loaded to the database system for analysis.