Abstract: Examples include bypassing a portion of an analytics workflow. In some examples, execution of an analytics workflow may be monitored upon receipt of a raw data and the execution may be interrupted at an optimal bypass stage to obtain insights data from the raw data. A similarity analysis may be performed to compare the insights data to a stored insights data in an insights data repository. Based, at least in part, on a determination of similarity, a bypass operation may be performed to bypass a remainder of the analytics workflow.

Abstract: A security framework for a multi-tenant, multi-tier computer system with embedded processing is described. A multi-tenant security framework is created for a combined processing and storage hierarchy of multiple tiers. The multi-tenant security framework is applied to multiple execution levels of the memory device. The multi-tenant security framework is applied to multiple layers of application server software of the memory device. The multi-tenant security framework is also applied to multiple layers of storage server software of the memory device.

Abstract: In some examples, with respect to distributed extent based replication, a plurality of logical unit number (LUN) extents may be ascertained for a local storage array including a LUN. A LUN owner may be assigned to the LUN. Respective access patterns used to access each of the plurality of LUN extents may be analyzed. Based on the analyzed access patterns, respective replication owners may be determined for each of the plurality of LUN extents independent of the LUN owner assigned to the LUN.

Abstract: Example implementations relate to a storage memory direct access (SMDA) provider. The SMDA provider may pin a storage memory region to a memory address of a consumer machine, the storage memory region corresponding to a storage range of a storage device requested by the consumer machine. The SMDA provider may atomically commit data in the storage memory region accessed by the consumer machine via the memory address.

Abstract: An example data storage system includes a first storage array having a first LUN and a second storage array having a second LUN. The first and second storage arrays may implement replication from the first LUN as a primary LUN to the second LUN as a secondary LUN. The first and second LUNs may both be an active target for host write I/O. The second storage array may, in response to receiving from a host a write that is directed to the second LUN, send write data of the write to the first storage for replication array while maintaining a copy of the write data in a fenced portion of a cache of the second storage array. The second storage array may wait to release the copy of the write data to the second LUN until a write acknowledgment is received from the first storage array.

Abstract: A non-transitory storage device includes machine readable instructions that, when executed, cause a processing resource to perform various operations. One such operation, for example, is to receive a selection of a blueprint to be used for configuration purposes. Other operations may include automatically validating the selected blueprint and automatically configuring the computing device in accordance with the selected and validated blueprint. Various related apparatuses and method are provided as well.

Abstract: Examples disclosed herein relate to a storage appliance using an optimistic allocation of storage space. In an example system, a number of storage drives are coupled to a storage controller and an RNIC (remote direct memory access (RDMA) network interface card (NIC)) through a storage network. The RNIC includes a layout template selector and a number of templates. The layout template selector selects a template based, at least in part, on a logical block address (LBA) received from a host. The template identifies each of a plurality of storage drives in the plurality of storage drives associated with portions of data represented by the LBA. A slave drive in the number of storage drives includes a state indicator that indicates the status of a transaction. A master drive in the number of storage drives also includes the state indicator to indicate the status of the transaction.

Abstract: Examples disclosed herein relate to a storage appliance using an optimistic allocation of storage space. In an example system, a number of storage drives are coupled to a storage controller and an RNIC (remote direct memory access (RDMA) network interface card (NIC)) through a storage network. The RNIC includes a layout template selector and a number of templates, wherein the layout template selector selects a template based, at least in part, on a logical block address (LBA) received from a host. The template identifies each of the plurality of storage drives associated with portions of data represented by the LBA.

Abstract: Examples disclosed herein relate to a storage appliance using an optimistic allocation of storage space. In an example system, a number of storage drives are coupled to a storage controller and a RNIC (remote direct memory access (RDMA) network interface card (NIC)) through a storage network. The RNIC includes a layout template selector and a plurality of templates. The layout template selector selects a layout template based, at least in part, on a logical block address (LBA) received from a host. The layout template identifies each of a number of storage drives associated with portions of data represented by the LBA. The controller includes a virtualization computation module to determine a new layout template for the data represented by the LBA and the new layout template.

Abstract: Relevance optimized representative content associated with a data storage system is disclosed. One example is a system including a data summarization module, a clustering module, and a representative content selection module. The data summarization module associates, via a processor, each data object in a storage system with a derived data object. The clustering module determines clusters of similar data objects based on a similarity between associated derived data objects, and selects a representative data object for each determined cluster. The representative content selection module selects representative content associated with the storage system, where the representative content is based on the data objects, the derived data objects, and the representative data objects, and relevance optimizes of the selected representative content to an analytics application.

Abstract: An example implementation may relate to an apparatus that may identify data content of interest from data in buffers, and may store index entries representing the identified data content in a hierarchical index having different performance levels. The apparatus may include a priority manager that maintains an index scoreboard that tracks where index entries are to be stored among the different performance levels of the hierarchical index based on predetermined polices that prioritize data content of interest or functions that use data content of interest.

Abstract: Examples disclosed herein relate to migration of data blocks. In an example, data blocks for migration from a source data storage device to a destination data storage device may be identified. A migration priority for each of the data blocks may be determined. The determination may comprise determining a plurality of parameters for each of the data blocks based on an analysis of respective input/output (I/O) operations of the data blocks in relation to a host system. The plurality of parameters may be provided as an input to an input layer of an artificial neural network engine. The input may be processed by a hidden layer of the artificial neural network engine. An output may be provided by an output layer of the artificial neural network engine. In an example, the output may include a migration priority for each of the data blocks.

Abstract: Example implementations relate to performing computations using provenance data. An example implementation includes storing first lineage data of a first dataset and provenance data of an application operating on the first dataset in a storage system. A computing resource may determine whether second lineage data of a second dataset meets a similarity criterion with the first lineage data of the first dataset. A computation on the second dataset may be performed using the provenance data of the application, and an insight of the second dataset may be generated from the performed computation.

Abstract: Example implementations may relate to a version controller allocating a copy page in persistent memory upon receiving, from an application executing on a processor, a copy command to version an image page for an atomic transaction. The version controller may receive application data addressed to a cache line of the image page, and may write the application data to a cache line of the copy page corresponding to the addressed cache line of the image page. If the version controller receives a replace-type transaction commit command, the version controller may generate a final page by either forward merging the image page into the copy page or backward merging the copy page into the image page, depending a merge direction policy.

Abstract: Examples relate to providing hardware assisted software versioning for clustered applications. In one example, virtualized global memory is accessible to application servers that provide a clustered application, where the clustered application includes multiple versions of a common data structure. After one of the application servers stores an element that is compatible with one version of the common data structure, other versions of the common data structure are located in the virtualized global memory. The element is then invalidated in the other versions of the common data structure to prevent access and translated directly in the virtualized global memory to the other versions of the common data structure. At this stage, the element can be validated in the other versions of the common data structure for access.

Abstract: According to an example, PM controller based atomicity assurance may include receiving data that is related to an application for storage in a PM. PM controller based atomicity assurance may further include receiving an indication of an atomic transaction that is related to the data, and receiving an indication to generate a CP that is related to the atomic transaction. The CP may be generated in a PM staging area. A determination may be made as to whether the CP in the PM staging area is closed or open. In response to a determination that the CP in the PM staging area is closed, content related to the CP may be propagated to the PM.

Abstract: Examples disclosed herein relate to relocation of an analytical process based on lineage metadata. In an example, a determination may be made, based on lineage metadata on a hub device, whether relocating an analytical process from the hub device to a remote edge device reduces execution time of the analytical process, wherein the analytical process is part of an analytical workflow that is implemented at least in part on the hub device and the remote edge device. In response to a determination that relocating the analytical process from the hub device to the remote edge device reduces the execution time of the analytical process, the analytical process may be relocated from the hub device to the remote edge device.

Abstract: Examples disclosed herein relate to data provisioning for an analytical process based on lineage metadata. In an example, a value of a parameter related to execution of an analytical process on a remote hub device may be determined based on lineage metadata stored on an edge device, wherein the analytical process is part of an analytical workflow that may be implemented at least in part on the edge device and the remote hub device. In response to a determination that the value of the parameter is above a predefined threshold, the edge device may provide to the remote hub device, input data for a future execution of the analytical process in advance of execution of the analytical process on the remote hub device without a request for the input data by the remote hub device.

Abstract: Examples disclosed herein relate to re-execution of an analytical process based on lineage metadata. In an example, a determination may be made on a hub device that an analytical process previously executed on a remote edge device is to be re-executed on the hub device, wherein the analytical process is part of an analytical workflow that is implemented at least in part on the hub device and the remote edge device. In response to the determination, a storage location of input data for re-executing the analytical process may be identified based on lineage metadata stored on the hub device, and input data may be acquired from the storage location.

Abstract: An example implementation in accordance with an aspect of the present disclosure includes an address translation table of a transparent router. In response to an add service request to add a service to at least a portion of a memory system, a first address to be affected by the service is identified. An affected entry of an address translation table of the transparent router is also identified. The affected entry corresponds to a data path including the first address. The affected entry is modified to cause its corresponding data path to point to a second address associated with the service.