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: Described herein are systems and methods for providing data sets from a constantly changing database to a streaming machine learning component. In one embodiment, a data streaming sub-system receives multiple incoming streams of data sets, in which each stream is generated in real-time by one of multiple data sources. The streaming sub-system sends data sets, on-the-fly as they are received, to storage in the memory of a database, in which there is a linkage between the storage and the time of arrival or the time of storage, of the data sets. The database receives, from a machine learning component, a request to receive data sets according to a particular time or time period. In response to such request, the database identifies such data sets according to the particular time or time period and sends them to the machine learning component.

Abstract: Described herein are systems and methods for providing data sets from a constantly changing database to a streaming machine learning component. In one embodiment, a data streaming sub-system receives multiple incoming streams of data sets, in which each stream is generated in real-time by one of multiple data sources. The streaming sub-system sends data sets, on-the-fly as they are received, to storage in the memory of a database, in which there is a linkage between the storage and the time of arrival or the time of storage, of the data sets. The database receives, from a machine learning component, a request to receive data sets according to a particular time or time period. In response to such request, the database identifies such data sets according to the particular time or time period and sends them to the machine learning component.

Abstract: Described herein are systems and methods for executing efficiently, in real-time, a plurality of machine learning processes. In one embodiment, a computing platform with multiple compute elements receives multiple data streams, each such stream associated with its own respective machine learning process. Each machine learning process is operative to use its data stream as input to train, in real-time, a respective mathematical model. Each of the processes has peaks and dips in processing demands. The system re-allocates, in real-time, compute elements from the processes with lower processing demands to processes with higher processing demands, thereby handling all of the multiple processes on-the-fly, preventing peak demands from causing the system to stall, and reducing overall the computational resources required by the system.

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 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, 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, 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: Described herein are systems and methods for executing efficiently, in real-time, a plurality of machine learning processes. In one embodiment, a computing platform with multiple compute elements receives multiple data streams, each such stream associated with its own respective machine learning process. Each machine learning process is operative to use its data stream as input to train, in real-time, a respective mathematical model. Each of the processes has peaks and dips in processing demands. The system re-allocates, in real-time, compute elements from the processes with lower processing demands to processes with higher processing demands, thereby handling all of the multiple processes on-the-fly, preventing peak demands from causing the system to stall, and reducing overall the computational resources required by the system.

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: Embodiments are directed to a process and system for optimizing traffic paths for orphaned hosts in a VXLAN system, by configuring virtual link trunking (VLT) peers to advertise MAC addresses learned from all multi-homed hosts in the system using Anycast VXLAN tunnel endpoint-Internet Protocol address (VTEP-IP); configuring the virtual link trunking (VLT) peers to advertise MAC addresses learned from all single-homed hosts in the system using a secondary VTEP-IP; directing unicast traffic destined to the single-homed hosts to directly connected VLT peers using the Secondary VTEP-IP; and directing Broadcast, unknown unicast, and multi-cast (BUM) traffic destined to the single-homed hosts to directly connected VLT peers using the Inclusive Multicast Ethernet Tag route.

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: 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.