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: 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: 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: Some examples relate to associating an insight with data. In an example, data may be received. A determination may be made that data type of the data is same as compared to an earlier data. An insight generated from the earlier data may be identified, wherein the insight may represent intermediate or resultant data generated upon processing of the earlier data by an analytics function, and wherein during generation metadata is associated with the insight. An analytics function used for generating the insight may be identified.

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 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: 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: A plurality of mapping modes may be shifted between in real time while maintaining continuous memory mapped access to an application. Data may be migrated between different types storage devices and/or interconnects. The shift between the plurality of mapping modes may be based on a change to the type storage device and/or type of interconnect for the data migration.

Abstract: Various examples described herein provide for migrating a file between a non-persistent memory file system and a persistent memory file system. In particular, some examples detect, on a non-persistent memory file system, a file that has been memory-mapped while being accessed from the non-persistent memory file system by a computer system, and the detected file is migrated from the non-persistent memory file system to the persistent memory file system.

Abstract: Embodiments herein relate to selecting an application server to nonvolatile random access memory (NVRAM) path. The path provides direct access between an application server and the NVRAM. The path is to transmit data related to at least one of a read and write request to a virtual memory of the application server.

Abstract: Various examples described herein provide for caching a page on persistent memory for memory-mapped access of a file from a non-persistent memory file system or a remote file system having a non-persistent memory page cache. In particular, some examples detect memory-mapped access of a file from a non-persistent memory file system or a remote file system having a non-persistent memory page cache and, based on availability of persistent memory, caches a page associated with the memory-mapped access on the persistent memory.

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: 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.

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: A memory access request including an address is received from a memory controller of an application server. One of a plurality of paths to the NVRAM is selected based on the address from the memory access request.

Abstract: A property is determined of an object in nonvolatile memory (NVM) of a device after the device is restarted. An action is performed on the NVM object based on the determined property of the NVM object. The property includes at least one of a volatility and executable information of the NVM object.

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: Example implementations relate to defining a first placement plan to place virtual storage appliance virtual machines on servers and defining a second placement plan to place an application virtual machine on the servers. The first placement plan can place each virtual storage appliance virtual machine on a server that is connected to a storage asset used by a respective VSA virtual machine.