Abstract: Apparatus, media, methods, and systems for data storage systems and methods for autonomously adapting data storage system performance, lifetime, capacity and/or operational requirements. A data storage system may comprise a controller and one or more non-volatile memory devices. The controller is configured to determine a category for a workload of one or more operations being processed by the data storage system using a machine-learned model. The controller is configured to determine an expected degradation of the one or more non-volatile memory devices. The controller is configured to adjust, based on the expected degradation and an actual usage of physical storage of the data storage system by a host system, an amount of physical storage of the data storage system available to the host system.

Abstract: Systems and methods for solid-state storage drive-level failure prediction and health metric are described. A plurality of host-write commands are received at a solid-state storage device. A number of drive-writes per day based on the on the plurality of host-write commands is determined. An aggregated amount of degradation to one or more internal non-volatile memory components based on the number of drive-writes per day is determined. Using a machine-learned model, a probability of failure value based on a set of parameter data and the aggregated amount of degradation to the non-volatile memory component is generated. An alert is generated, based on the probability of failure value or degradation threshold.

Abstract: Apparatus, media, methods, and systems for data storage systems and methods for autonomously adapting data storage system performance, lifetime, capacity and/or operational requirements. A data storage system may comprise a controller and one or more non-volatile memory devices. The controller is configured to determine a category for a workload of one or more operations being processed by the data storage system using a machine-learned model. The controller is configured to determine an expected degradation of the one or more non-volatile memory devices. The controller is configured to adjust, based on the expected degradation and an actual usage of physical storage of the data storage system by a host system, an amount of physical storage of the data storage system available to the host system.

Abstract: Apparatus, media, methods, and systems for data storage systems and methods for autonomously adapting data storage system performance, lifetime, capacity and/or operational requirements. A data storage system may comprise a controller and one or more non-volatile memory devices. The controller is configured to determine a category for a workload of one or more operations being processed by the data storage system using a machine-learned model. The controller is configured to determine an expected degradation of the one or more non-volatile memory devices. The controller is configured to adjust, based on the expected degradation and an actual usage of physical storage of the data storage system by a host system, an amount of physical storage of the data storage system available to the host system.

Abstract: Apparatus, media, methods, and systems for data storage systems and methods for autonomously adapting data storage system performance, lifetime, capacity and/or operational requirements. A data storage system may comprise a controller and one or more non-volatile memory devices. The controller is configured to determine a category for a workload of one or more operations being processed by the data storage system using a machine-learned model. The controller is configured to determine an expected degradation of the one or more non-volatile memory devices. The controller is configured to adjust, based on the expected degradation and an actual usage of physical storage of the data storage system by a host system, an amount of physical storage of the data storage system available to the host system.

Abstract: Systems and methods for solid-state storage drive-level failure prediction and health metric are described. A plurality of host-write commands are received at a solid-state storage device. A number of drive-writes per day based on the on the plurality of host-write commands is determined. An aggregated amount of degradation to one or more internal non-volatile memory components based on the number of drive-writes per day is determined. Using a machine-learned model, a probability of failure value based on a set of parameter data and the aggregated amount of degradation to the non-volatile memory component is generated. An alert is generated, based on the probability of failure value or degradation threshold.

Abstract: Systems and methods for solid-state storage drive-level failure prediction and health metric are described. A plurality of host-write commands are received at a solid-state storage device. A number of drive-writes per day based on the on the plurality of host-write commands is determined. An aggregated amount of degradation to one or more internal non-volatile memory components based on the number of drive-writes per day is determined. Using a machine-learned model, a probability of failure value based on a set of parameter data and the aggregated amount of degradation to the non-volatile memory component is generated. An alert is generated, based on the probability of failure value or degradation threshold.

Abstract: Apparatus, media, methods, and systems for data storage systems and methods for autonomously adapting data storage system performance, lifetime, capacity and/or operational requirements. A data storage system may comprise a controller and one or more non-volatile memory devices. The controller is configured to determine a category for a workload of one or more operations being processed by the data storage system using a machine-learned model. The controller is configured to determine an expected degradation of the one or more non-volatile memory devices. The controller is configured to adjust, based on the expected degradation and an actual usage of physical storage of the data storage system by a host system, an amount of physical storage of the data storage system available to the host system.

Abstract: Systems and methods for solid-state storage drive-level failure prediction and health metric are described. A plurality of host-write commands are received at a solid-state storage device. A number of drive-writes per day based on the on the plurality of host-write commands is determined. An aggregated amount of degradation to one or more internal non-volatile memory components based on the number of drive-writes per day is determined. Using a machine-learned model, a probability of failure value based on a set of parameter data and the aggregated amount of degradation to the non-volatile memory component is generated. An alert is generated, based on the probability of failure value or degradation threshold.