Western Digital Technologies, Inc.

Evan Richardson

Bernd Lamberts

Remmelt Pit

Jeffrey R Hobbet

Timothy G Lieber

Robert Lester

Austin Striegel

Donald Penza

Ravi I Singh

Maulik Thaker

Jared Tesone

An approach to exchanging information between data storage devices (DSDs) within a secure data center and an external fleet health manager (FHM) application includes querying one or more DSDs for data to be analyzed, including providing a unique query identifier, whereby a particular DSD responsively provides (i) a device identifier identifying itself, (ii) a random key code for authentication and integrity purposes, (iii) the data to be analyzed, and (iv) the query identifier for the FHM application to verify. The FHM application can then digitally sign a corrective action payload, using the key code from the particular DSD, including the query identifier and the device identifier and a recommended corrective action, and transmit the signed corrective action payload to the data center for application to the particular DSD, whereby the DSD can execute pre-defined fundamental repair action operation(s) corresponding to the corrective action for in-situ repair.

Fleet health management corrective action communication exchange

An approach to identifying a corrective action for a data storage device (DSD), such as one implemented in a fleet of DSDs in a data center, involves receiving error data about excursions from normal operational behavior of the DSD, inputting data representing a particular excursion into a probabilistic decision network which characterizes a set of DSD operational metrics and certain DSD controller rules that represent internal controls of the DSD and corresponding conditional relationships among the operational metrics, determining from the decision network the likelihood that one or more possible causes was a contributing factor to the particular excursion, and determining a corrective action for the particular excursion based on the determined likelihood of a particular cause of the one or more possible causes. The corrective action may then be shared with the DSD for in-situ execution of corresponding self-repair operations.

Fleet health management device classification framework

An approach to identifying poorly performing data storage devices (DSDs) in a data storage system, such as hard disk drives (HDDs) and/or solid-state drives (SSDs), involves retrieving and evaluating a respective set of log pages, such as SCSI Log Sense counters, from each of multiple DSDs. Based on each respective set of log pages, a value for a Quality of Service (QoS) metric is determined for each respective DSD, where each QoS value represents an average percentage of bytes processed without the respective DSD performing an autonomous error correction. In response to a particular DSD reaching a predetermined threshold QoS value, an in-situ repair may be determined for the particular DSD or the particular DSD may be added to a list of candidate DSDs for further examination, which may include an FRPH examination for suitably configured DSDs.

Use of error correction-based metric for identifying poorly performing data storage devices

An approach to identifying problematic data storage devices, such as hard disk drives (HDDs), in a data storage system involves retrieving and evaluating a respective recovery log, such as a media error section of a device status log, from each of multiple HDDs. Based on each recovery log, a value for a Full Recoveries Per Hour (FRPH) metric is determined for each read-write head of each respective HDD. Generally, the FRPH metric characterizes the amount of time a head has spent performing recovery operations. In response to a particular head FRPH reaching a pre-determined threshold value, an in-situ repair can be determined for the HDD in which the head operates. Similarly, in the context of solid-state drives (SSDs), a latency metric is determinable based on time spent waiting on resolving input/output (IO) request collisions, on which an in-situ repair can be based.

Use of recovery behavior for prognosticating and in-situ repair of data storage devices

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Rochester, MN, United States of America

Evan Richardson