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: 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.