Abstract: With a forever incremental snapshot configuration and a typical caching policy (e.g., least recently used), a storage appliance may evict stable data blocks of an older snapshot, perhaps unchanged data blocks of the snapshot baseline. If stable data blocks have been evicted, restore of a recent snapshot will suffer the time penalty of downloading the stable blocks for restoring the recent snapshot. Creating synthetic baseline snapshots and refreshing eviction data of stable data blocks can avoid eviction of stable data blocks and reduce the risk of violating a recovery time objective.

Abstract: Embodiments relate to the autonomous remediation of IT asset records in accordance with rules-based asset management policies. An IT asset record associated with an IT asset and having a first piece of IT asset information is received and evaluated for compliance in accordance with defined asset management rules. The asset management rules require the IT asset record to include both the first piece and a second piece of IT asset information. In view of the rules, a determination is made that the IT asset record requires remediation. Based on a determination that the IT asset record requires remediation, a decision-tree machine learning model is utilized to infer, based on IT asset information available in the IT asset record, at least the second piece of IT asset information to place the IT asset record in compliance with the asset management rules.

Abstract: A storage appliance arranges snapshot data and snapshot metadata into different structures, and arranges the snapshot metadata to facilitate efficient snapshot manipulation, which may be for snapshot management or snapshot restore. The storage appliance receives snapshots according to a forever incremental configuration and arranges snapshot metadata into different types of records. The storage appliance stores these records in key-value stores maintained for each defined data collection (e.g., volume). The storage appliance arranges the snapshot metadata into records for inode information, records for directory information, and records that map source descriptors of data blocks to snapshot file descriptors. The storage appliance uses a locally generated snapshot identifier as a key prefix for the records to conform to a sort constrain of the key-value store, which allows the efficiency of the key-value store to be leveraged.

Abstract: A storage appliance arranges snapshot data and snapshot metadata into different structures, and arranges the snapshot metadata to facilitate efficient snapshot manipulation, which may be for snapshot management or snapshot restore. The storage appliance receives snapshots according to a forever incremental configuration and arranges snapshot metadata into different types of records. The storage appliance stores these records in key-value stores maintained for each defined data collection (e.g., volume). The storage appliance arranges the snapshot metadata into records for inode information, records for directory information, and records that map source descriptors of data blocks to snapshot file descriptors. The storage appliance uses a locally generated snapshot identifier as a key prefix for the records to conform to a sort constrain of the key-value store, which allows the efficiency of the key-value store to be leveraged.

Abstract: A computer-implemented method for modelling a driveline, the driveline comprising a plurality of components. The method comprising the steps of: a) receiving a parametric description of the driveline; b) creating a thermal model of the driveline from the parametric description; c) calculating a temperature distribution for one or more components of the driveline using the thermal model; d) determining a deflection of one or more components of the driveline caused by the thermal distribution, based on the parametric description and the temperature distribution; and e) calculating a performance metric of the driveline based on the determined deflection of the one or more components.

Abstract: With a forever incremental snapshot configuration and a typical caching policy (e.g., least recently used), a storage appliance may evict stable data blocks of an older snapshot, perhaps unchanged data blocks of the snapshot baseline. If stable data blocks have been evicted, restore of a recent snapshot will suffer the time penalty of downloading the stable blocks for restoring the recent snapshot. Creating synthetic baseline snapshots and refreshing eviction data of stable data blocks can avoid eviction of stable data blocks and reduce the risk of violating a recovery time objective.

Abstract: With a forever incremental snapshot configuration and a typical caching policy (e.g., least recently used), a storage appliance may evict stable data blocks of an older snapshot, perhaps unchanged data blocks of the snapshot baseline. If stable data blocks have been evicted, restore of a recent snapshot will suffer the time penalty of downloading the stable blocks for restoring the recent snapshot. Creating synthetic baseline snapshots and refreshing eviction data of stable data blocks can avoid eviction of stable data blocks and reduce the risk of violating a recovery time objective.

Abstract: A method for designing a gearset meeting one or more design targets is provided. In particular, the design target is efficiency. Initially, a size and ratio of the gear set is specified. Gear friction coefficients are then calculated, and a value for a design target for the gear set is calculated. One or more of the macro-geometry parameters are modified, and the macro-geometry parameters are chosen such that the advantageous effects of one macro-geometry parameter on the design target counteract any disadvantageous effects of another macro-geometry parameter. This allows for a design for a gearset meeting the one or more design targets to be produced. The efficiency calculation includes the effect of a lubricant. Further design targets can include gear noise and gear durability.

Abstract: A computer-implemented system is disclosed for producing a design for a rotating machine assembly. It comprises a data module (10) configured for receiving data relating to one or more components of the rotating machine assembly; a user interface module (20 configured for specifying data to be received by the data module and for receiving from a user a type of analysis to be performed on the data; and an analysis module (30) configured for analysing a performance of the rotating machine assembly according to the type of analysis selected and selected features of the data to be used. It further includes a recognition module (40) configured for identifying and selected features of the data be used for the analysis according to the type of analysis selected.

Abstract: A method, non-transitory computer readable medium, and device that assists with managing cloud storage includes identifying a portion of data in a data unit identified for deletion in the metadata. The identified portion of the data identified for delete is compare to a threshold amount. Deletion of the data unit from a first storage object is deferred when the determined portion of data identified for deletion is less than the threshold amount. A second storage object with a portion of data unmarked for deletion in the data unit is generated when the determined portion of data marked for deletion is equal to the threshold amount, wherein the second storage object has a same identifier as the first storage object.

Abstract: A storage appliance arranges snapshot data and snapshot metadata into different structures, and arranges the snapshot metadata to facilitate efficient snapshot manipulation, which may be for snapshot management or snapshot restore. The storage appliance receives snapshots according to a forever incremental configuration and arranges snapshot metadata into different types of records. The storage appliance stores these records in key-value stores maintained for each defined data collection (e.g., volume). The storage appliance arranges the snapshot metadata into records for inode information, records for directory information, and records that map source descriptors of data blocks to snapshot file descriptors. The storage appliance uses a locally generated snapshot identifier as a key prefix for the records to conform to a sort constrain of the key-value store, which allows the efficiency of the key-value store to be leveraged.

Abstract: A computer-implemented method for modelling a driveline, the driveline comprising a plurality of components. The method comprising the steps of: a) receiving a parametric description of the driveline; b) creating a thermal model of the driveline from the parametric description; c) calculating a temperature distribution for one or more components of the driveline using the thermal model; d) determining a deflection of one or more components of the driveline caused by the thermal distribution, based on the parametric description and the temperature distribution; and e) calculating a performance metric of the driveline based on the determined deflection of the one or more components.

Abstract: A storage appliance arranges snapshot data and snapshot metadata into different structures, and arranges the snapshot metadata to facilitate efficient snapshot manipulation, which may be for snapshot management or snapshot restore. The storage appliance receives snapshots according to a forever incremental configuration and arranges snapshot metadata into different types of records. The storage appliance stores these records in key-value stores maintained for each defined data collection (e.g., volume). The storage appliance arranges the snapshot metadata into records for inode information, records for directory information, and records that map source descriptors of data blocks to snapshot file descriptors. The storage appliance uses a locally generated snapshot identifier as a key prefix for the records to conform to a sort constrain of the key-value store, which allows the efficiency of the key-value store to be leveraged.

Abstract: A computer-implemented method for modelling a driveline. The driveline comprising a plurality of components. The method comprising the steps of: a) receiving a parametric description of the driveline; b) creating a tribology model of the driveline from the parametric description; c) calculating one or more traction coefficients for one or more components of the driveline using the tribology model; and d) calculating a performance metric of the driveline, based on the parametric description and the one or more traction coefficients.

Abstract: A storage appliance can be designed to facilitate efficient restore of multiple backed up files in a system that allows files to share data blocks. A data management application or storage OS names data blocks and communicates those names to the storage appliance when backing up to or through the storage appliance. The storage appliance can leverage the data block names when restoring a group of files by restoring at data block granularity instead of file granularity. Restoring at the granularity of the data blocks by their names allows the storage appliance to avoid repeatedly sending a same data block to the restore requestor (e.g., a storage OS or data management application) while still instructing the restore requestor how to reconstruct the corresponding file(s) with mappings between valid data ranges and the named data blocks.

Abstract: A method, non-transitory computer readable medium, and device that assists with managing cloud storage includes identifying a portion of data in a data unit identified for deletion in the metadata. The identified portion of the data identified for delete is compare to a threshold amount. Deletion of the data unit from a first storage object is deferred when the determined portion of data identified for deletion is less than the threshold amount. A second storage object with a portion of data unmarked for deletion in the data unit is generated when the determined portion of data marked for deletion is equal to the threshold amount, wherein the second storage object has a same identifier as the first storage object.

Abstract: With a forever incremental snapshot configuration and a typical caching policy (e.g., least recently used), a storage appliance may evict stable data blocks of an older snapshot, perhaps unchanged data blocks of the snapshot baseline. If stable data blocks have been evicted, restore of a recent snapshot will suffer the time penalty of downloading the stable blocks for restoring the recent snapshot. Creating synthetic baseline snapshots and refreshing eviction data of stable data blocks can avoid eviction of stable data blocks and reduce the risk of violating a recovery time objective.

Abstract: The present invention is a computer-implemented method for designing a drivetrain using computer aided engineering. The method comprises the steps of: a) receiving a selection of a certification test from a plurality of certification tests; b) receiving a parametric description of the drivetrain; c) receiving a selection of one or more analysis types appropriate for the selected certification test and the parametric description; d) providing a certification test model of the drivetrain based on the selected certification test, the parametric description, and the one or more analysis types; e) simulating performance of the drivetrain using the certification test model; and f) determining an indication of compliance of the drivetrain with the certification test based on the simulated performance.

Abstract: With a forever incremental snapshot configuration and a typical caching policy (e.g., least recently used), a storage appliance may evict stable data blocks of an older snapshot, perhaps unchanged data blocks of the snapshot baseline. If stable data blocks have been evicted, restore of a recent snapshot will suffer the time penalty of downloading the stable blocks for restoring the recent snapshot. Creating synthetic baseline snapshots and refreshing eviction data of stable data blocks can avoid eviction of stable data blocks and reduce the risk of violating a recovery time objective.

Abstract: Intelligent snapshot tiering facilitates efficient management of snapshots and efficient restore of snapshots. For intelligent snapshot tiering, a storage appliance can limit cross-tier migration to invalidated data blocks of a snapshot instead of an entire snapshot. Based on a policy, a storage appliance can identify a snapshot to be migrated to another storage tier and then determine which data blocks are invalidated by an immediately succeeding snapshot. This would limit network bandwidth consumption to the invalidated data blocks and maintain the valid data blocks at the faster access storage tier since the more recent snapshots are more likely to be restored.