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 computer-implemented method according to one embodiment includes causing a plurality of I/O queues to be created between an initiator and a storage target device. The created I/O queues are reserved for I/O requests for which adjustments of current priorities of extents of data associated with the I/O requests are to be performed. The method further includes determining identifying information of an I/O request sent from the initiator to the storage target device and determining whether the I/O request was sent from the initiator to the storage target device using one of the created I/O queues. In response to a determination that the I/O request was sent using a first of the created I/O queues having one of the adjustments associated therewith, a tiering manager of the storage target device is instructed to perform the adjustment on the current priority of the extent of data associated with the I/O request.

A method for computational storage may include storing, at a storage device, two or more portions of data, wherein a first one of the two or more portions of data comprises a first fragment of a record and a second one of the two or more portions of data comprises a second fragment of the record, and performing, by the storage device, an operation on the first and second fragments of the record. The method may further include performing, by the storage node, a second operation on first and second fragments of a second record. The operation may include a data selection operation, and the method may further include sending a result of the data selection operation to a server. The method may further include sending a result of a first data selection operation to a server.

A computer-implemented method, according to one embodiment, includes: storing records in an input data buffer, where each of the records include a key which is appended to payload data in the respective record. Moreover, for each of the records: shearing the key associated with the record from the payload data, normalizing the sheared key, and storing the normalized sheared key in a first target area of memory. A determination is made as to whether a size of the payload data in the record is outside a predetermine range, and in response to determining that the size of the payload data in the record is outside the predetermine range, the payload data is stored in a second target area of memory. A data locator is also appended to the normalized sheared key in the first target area of memory to form a sheared record.

A method for computational storage may include storing, at a storage device, a first portion of data, wherein the first portion of data may include a first fragment of a record, and a second portion of data may include a second fragment of the record, and appending the second fragment of the record to the first portion of data. The method may further include performing, at the storage device, an operation on the first and second fragments of the record. The method may further include determining that the first portion of data may include a first fragment of a record, and a second portion of data may include a second fragment of the record, wherein appending the second fragment of the record to the first portion of data may include appending, based on the determining, the second fragment of the record to the first portion of data.

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 storage system. In some embodiments, the storage system includes a plurality of object stores, and a plurality of data managers, connected to the object stores. The plurality of data managers may include a plurality of processing circuits. A first processing circuit of the plurality of processing circuits may be configured to process primarily input-output operations, and a second processing circuit of the plurality of processing circuits may be configured to process primarily input-output completions.

Method and system for training a machine learning model based on a training dataset formed by data objects distributed across a virtual object storage service. The method comprises fetching from the virtual object storage service, the training dataset; copying the fetched training dataset on a first local storage device and maintaining a list of modifications executed on the training dataset that occurred on the virtual object storage service. The method comprises, upon receiving a request to initiate training of the machine learning model, generating a synchronized training dataset mirroring the training dataset stored in the virtual object storage service; storing the synchronized training dataset in a second local storage device; and fetching training data from the synchronized training dataset stored in the second local storage device as the training of the machine learning model is executed.

A method of storing data, a method of reading data, a device, and a storage medium are provided, which relate to a field of artificial intelligence, in particular to the fields of cloud computing technology and distributed storage technology. A specific implementation scheme includes: storing at least one target data into a target file in a storage class memory device; recording a storage address of the at least one target data in the storage class memory device in a dynamic random access memory as a first index data; and synchronously storing the first index data into the storage class memory device as a second index data.

A method for computational storage may include receiving, at a storage device, a modified version of a portion of data, generating, at the storage device, a restored portion of data from the modified version of the portion of data, and performing, at the storage device, an operation on the restored portion of data. The method may further include receiving, at the storage device, a request to perform the operation on the portion of data. The generating may include decompressing the modified version of the portion of data. The generating may include decrypting the modified version of the portion of data. The method may further include sending, from the storage device, a result of the operation on the restored portion of data. The operation may include a filtering operation. The operation may include a scanning operation. The method may further include dividing data to generate the portion of data.