An apparatus comprises at least one processing device configured to identify a snapshot lineage comprising snapshots of a storage volume, the snapshot lineage comprising (i) a local snapshot lineage stored on a storage system and (ii) cloud snapshot lineages stored on cloud storage external to the storage system, to select at least one snapshot that is to be copied from the local snapshot lineage, to determine at least two of the cloud snapshot lineages as destinations for the selected snapshot, to generate a snapshot copy job for copying the selected snapshot to the at least two cloud snapshot lineages, and to process the snapshot copy job by reading data of the selected snapshot stored in the local snapshot lineage once and writing the data of the selected snapshot to the at least two cloud snapshot lineages.

A data management and storage (DMS) cluster of peer DMS nodes manages data of a tenant of a multi-tenant compute infrastructure. The compute infrastructure includes an envoy connecting the DMS cluster to virtual machines of the tenant executing on the compute infrastructure. The envoy provides the DMS cluster with access to the virtual tenant network and the virtual machines of the tenant connected via the virtual tenant network for DMS services such as data fetch jobs to generate snapshots of the virtual machines. The envoy sends the snapshot from the virtual machine to a peer DMS node via the connection for storage within the DMS cluster. The envoy provides the DMS cluster with secure access to authorized tenants of the compute infrastructure while maintaining data isolation of tenants within the compute infrastructure.

A data management and storage (DMS) cluster of peer DMS nodes manages data of a tenant of a multi-tenant compute infrastructure. The compute infrastructure includes an envoy connecting the DMS cluster to virtual machines of the tenant executing on the compute infrastructure. The envoy provides the DMS cluster with access to the virtual tenant network and the virtual machines of the tenant connected via the virtual tenant network for DMS services such as data fetch jobs to generate snapshots of the virtual machines. The envoy sends the snapshot from the virtual machine to a peer DMS node via the connection for storage within the DMS cluster. The envoy provides the DMS cluster with secure access to authorized tenants of the compute infrastructure while maintaining data isolation of tenants within the compute infrastructure.

Embodiments of the invention are directed to database replication using heterogenous encoding. Aspects include obtaining a database and analyzing a data pattern of data in the database. Aspects also include identifying a plurality of candidate encoding formats and evaluating a computing cost for encoding the database for each of the plurality of candidate encoding formats. Aspects further include selecting an encoding format from the plurality of candidate encoding formats based at least in part on the computing cost and storing a backup copy of the database using the encoding format.

Embodiments for a data protection method of grouping assets for protection policy assignment based on asset metadata by defining a set of metrics characterizing each asset in the system and comparing each metric of an asset with corresponding metrics of other asset groups each containing one or more other assets. A unique protection policy is assigned to each group for application to each asset within a respective group. An overall affinity percentage of the metrics of asset with the corresponding metrics of each group is determined, and the asset is automatically grouped into the group based with the highest overall affinity percentage. The user is prompted to confirm the automatic grouping or to select a different group for assigning to the asset.

A storage system has a plurality of storage nodes having equal non-volatile storage capacity that is subdivided into equal size cells. Host application data that is stored in the cells is protected using RAID or EC protection groups each having members stored in ones of the cells and distributed across the storage nodes such that no more than one member of any single protection group is stored by any one of the storage nodes. Spare cells are maintained for rebuilding protection group members of a failed one of the storage nodes on remaining non-failed storage nodes so full data access is possible before replacement or repair of the failed storage node.

The disclosed embodiments relate to securely booting firmware images. In one embodiment, a method is disclosed comprising receiving, by a memory device, a firmware update; validating, by the memory device, a signature associated with the firmware update; copying, by the memory device, an existing firmware image to an archive location, the archive location storing a plurality of firmware images sorted by version identifiers; booting, by the memory device, and executing the firmware update; and replacing, by the memory device, the firmware update with the existing firmware image stored in the archive location upon detecting an error while booting the firmware update.

An electronic device includes a display, a memory, and a processor operatively connected to the display and the memory. The memory stores instructions that, when executed, cause the processor to receive backup data from an external electronic device, identify characteristics of the external electronic device based on the backup data, identify characteristics of the electronic device based on the backup data, at least partially change the backup data based on the characteristics of the electronic device, and display a result of performing a restoring operation based on the at least partially changed backup data on the display.

There are provided systems and methods for a compute platform for machine leaning model roll-out. A service provider, such as an electronic transaction processor for digital transactions, may provide intelligent decision-making through decision services that execute machine learning models. When deploying or updating machine learning models in these engines and decision services, a model package may include multiple models, each of which may have an execution graph required for model execution. When models are tested from proper execution, the models may have non-performant compute items, such as model variables, that lead to improper execution and/or decision-making. A model deployer may determine and flag these compute items as non-performant and may cause these compute items to be skipped or excluded from execution. Further, the model deployer may utilize a pre-production computing environment to generate the execution graphs for the models prior to deployment or upgrading.

An approach for staged data backup is provided. The approach comprises downloading data to be backed up, by one or more processors, from at least one of cloud computing servers in a plurality of stages into a local cache, wherein each of the plurality of stages corresponds to a time point when a corresponding downloading operation starts, and a time point corresponding to the last stage of the plurality of stages is the time point when a request to back up the data is received. The approach comprises merging, by one or more processors, downloaded data corresponding to each of the plurality of stages into up-to-date data. The approach further comprises saving, by one or more processors, the merged up-to-date data corresponding to the last stage as a backup dataset.