Abstract: Apparatuses, systems, methods, and computer program products are disclosed for persistent memory management. Persistent memory management may include replicating a persistent data structure in volatile memory buffers of at least two non-volatile storage devices. Persistent memory management may include preserving a snapshot copy of data in association with completion of a barrier operation for the data. Persistent memory management may include determining which interface of a plurality of supported interfaces is to be used to flush data from a processor complex.

Abstract: Apparatuses, systems, methods, and computer program products are disclosed for persistent memory management. Persistent memory management may include replicating a persistent data structure in volatile memory buffers of at least two non-volatile storage devices. Persistent memory management may include preserving a snapshot copy of data in association with completion of a barrier operation for the data. Persistent memory management may include determining which interface of a plurality of supported interfaces is to be used to flush data from a processor complex.

Abstract: Apparatuses, systems, and methods are disclosed for snapshots of a non-volatile device. A method includes writing data in a sequential log structure for a non-volatile device. A method includes marking a point, in a sequential log structure, for a snapshot of data. A method includes preserving a logical-to-physical mapping for a snapshot based on a marked point and a temporal order for data in a sequential log structure.

Abstract: Apparatuses, systems, program products, and method are disclosed for machine learning abstraction. An apparatus includes an objective module configured to receive an objective to be analyzed using machine learning. An apparatus includes a grouping module configured to select a logical grouping of one or more machine learning pipelines to analyze a received objective. An apparatus includes an adjustment module configured to dynamically adjust one or more machine learning settings for a logical grouping of one or more machine learning pipelines based on feedback generated in response to analyzing a received objective.

Abstract: An automated machine learning (“ML”) method may include training a first machine learning model using a first machine learning algorithm and a training data set; validating the first machine learning model using a validation data set, wherein validating the first machine learning model comprises generating an error data set; training a second machine learning model to predict a suitability of the first machine learning model for analyzing an inference data set, wherein the second machine learning model is trained using a second machine learning algorithm and the error data set; and triggering a remedial action associated with the first or second machine learning model in response to a predicted suitability of the first machine learning model for analyzing the inference data set not satisfying a suitability threshold.

Abstract: Data of a vector storage request pertaining to one or more disjoint, non-adjacent, and/or non-contiguous logical identifier ranges are stored contiguously within a log on a non-volatile storage medium. A request consolidation module modifies one or more sub-requests of the vector storage request in response to other, cached storage requests. Data of an atomic vector storage request may comprise persistent indicators, such as persistent metadata flags, to identify data pertaining to incomplete atomic storage requests. A restart recovery module identifies and excludes data of incomplete atomic operations.

Abstract: Apparatuses, systems, methods, and computer program products are disclosed for persistent memory management. Persistent memory management may include replicating a persistent data structure in volatile memory buffers of at least two non-volatile storage devices. Persistent memory management may include preserving a snapshot copy of data in association with completion of a barrier operation for the data. Persistent memory management may include determining which interface of a plurality of supported interfaces is to be used to flush data from a processor complex.

Abstract: Apparatuses, systems, methods, and computer program products are disclosed for a persistent data structure. A method includes associating a logical identifier with a data structure. A method includes writing data of a data structure to a first region of a volatile memory module. A volatile memory module may be configured to ensure that data is preserved in response to a trigger. A method includes copying data of a data structure from a volatile memory module to a non-volatile storage medium such that the data of the data structure remains associated with a logical identifier.

Abstract: Apparatuses, systems, methods, and computer program products are disclosed for persistent memory management. Persistent memory management may include providing a persistent data structure stored at least partially in volatile memory configured to ensure persistence of the data structure in a non-volatile memory medium. Persistent memory management may include replicating a persistent data structure in volatile memory buffers of at least two non-volatile storage devices. Persistent memory management may include preserving a snapshot copy of data in association with completion of a barrier operation for the data. Persistent memory management may include determining which interface of a plurality of supported interfaces is to be used to flush data from a processor complex.

Abstract: Apparatuses, systems, program products, and methods are disclosed for interpretability-based machine learning adjustment during production. An apparatus includes a first results module that is configured to receive a first set of inference results of a first machine learning algorithm during inference of a production data set. An apparatus includes a second results module that is configured to receive a second set of inference results of a second machine learning algorithm during inference of a production data set. An apparatus includes an action module that is configured to trigger one or more actions that are related to a first machine learning algorithm in response to a comparison of first and second sets of inference results not satisfying explainability criteria.

Abstract: A storage layer may be configured to over-provision logical storage resources to objects. The storage layer may provision the resources in response to, inter alia, a request to open and/or create a zero-length file. The storage layer may be further configured to store data of the objects in a contextual format configured to associate the data with respective logical identifiers. The storage layer may determine an actual, storage size of the object based on the associations stored on the stored associations. Storage clients may rely on the storage layer to determine the size of the object and, as such, may defer and/or eliminate updates to persistent metadata.

Abstract: Apparatuses, systems, program products, and methods are disclosed for determining validity of machine learning algorithms for datasets. An apparatus includes a primary training module that is configured to train a first machine learning model for a first machine learning algorithm. An apparatus includes a primary validation module that is configured to validate a first machine learning model to generate an error data set. An apparatus includes a secondary training module that is configured to train a second machine learning model for a second machine learning algorithm using an error data set. A second machine learning algorithm may be configured to predict a suitability of a first machine learning model for analyzing an inference data set. An apparatus includes an action module that is configured to trigger an action in response to a predicted suitability of the first machine learning model not satisfying a predetermined suitability threshold.

Abstract: An apparatus, system, and method are disclosed for data management. The method includes writing data in a sequential log structure. The method also includes receiving a time sequence request from a client. The method further includes servicing the time sequence request based on a temporal order of the data in the sequential log structure.

Abstract: Apparatuses, systems, program products, and method are disclosed for detecting suitability of machine learning models for datasets. An apparatus includes a training evaluation module configured to calculate a first statistical data signature for a training data set of a machine learning system using one or more predefined statistical algorithms. An apparatus includes an inference evaluation module configured to calculate a second statistical data signature for an inference data set of a machine learning system using one or more predefined statistical algorithms. An apparatus includes a score module configured to calculate a suitability score describing the suitability of a training data set to an inference data set as a function of a first and a second statistical data signature. An apparatus includes an action module configured to perform an action related to a machine learning system in response to a suitability score satisfying an unsuitability threshold.

Abstract: A system includes a data store and a memory cache subsystem. A method for pre-fetching data from the data store for the cache includes determining a performance characteristic of a data store. The method also includes identifying a pre-fetch policy configured to utilize the determined performance characteristic of the data store. The method also includes pre-fetching data stored in the data store by copying data from the data store to the cache according to the pre-fetch policy identified to utilize the determined performance characteristic of the data store.

Abstract: Apparatuses, systems, and methods are disclosed for snapshots of a non-volatile device. A method includes writing data in a sequential log structure for a non-volatile device. A method includes marking a point, in a sequential log structure, for a snapshot of data. A method includes preserving a logical-to-physical mapping for a snapshot based on a marked point and a temporal order for data in a sequential log structure.

Abstract: A storage system implements a sparse, thinly provisioned logical-to-physical translation layer. The storage system may perform operations to modify logical-to-physical mappings, including creating, removing, and/or modifying any-to-any and/or many-to-one mappings between logical identifiers and stored data (logical manipulation operations). The storage system records persistent metadata to render the logical manipulation (LM) operations persistent and crash-safe. The storage system may provide access to LM functionality through a generalized LM interface. Clients may leverage the LM interface to efficiently implement higher-level functionality and/or offload LM operations to the storage system.

Abstract: Data of a vector storage request pertaining to one or more disjoint, non-adjacent, and/or non-contiguous logical identifier ranges are stored contiguously within a log on a non-volatile storage medium. A request consolidation module modifies one or more sub-requests of the vector storage request in response to other, cached storage requests. Data of an atomic vector storage request may comprise persistent indicators, such as persistent metadata flags, to identify data pertaining to incomplete atomic storage requests. A restart recovery module identifies and excludes data of incomplete atomic operations.

Abstract: Apparatuses, systems, and methods are disclosed for snapshots of a non-volatile device. A method includes writing data in a sequential log structure for a non-volatile device. A method includes marking a point, in a sequential log structure, for a snapshot of data. A method includes preserving a logical-to-physical mapping for a snapshot based on a marked point and a temporal order for data in a sequential log structure.

Abstract: Techniques are disclosed relating to enabling virtual machines to access data on a physical recording medium. In one embodiment, a computing system provides a logical address space for a storage device to an allocation agent that is executable to allocate the logical address space to a plurality of virtual machines having access to the storage device. In such an embodiment, the logical address space is larger than a physical address space of the storage device. The computing system may then process a storage request from one of the plurality of virtual machines. In some embodiments, the allocation agent is a hypervisor executing on the computing system. In some embodiments, the computing system tracks utilizations of the storage device by the plurality of virtual machines, and based on the utilizations, enforces a quality of service level associated with one or more of the plurality of virtual machines.