Pure Storage, Inc.

Prakash Darji

Andrew R Bernat

Shvetima Gulati

Ronald S Karr

Robert Barker, Jr

Jonas R Irwin

Chadd Kenney

Charles Giancarlo

Steven Ma

Taher Vohra

Lawrence Mertes

Payal Jauhar Srivastava

Profiling user activity to achieve social and governance objectives, including: generating, based on data describing activities within a system, a plurality of activity groupings each including one or more user accounts and corresponding to a particular activity in the system; generating, for each of the plurality of activity groupings, one or more social groupings based on user profiles of the one or more user accounts in a corresponding activity grouping, wherein each of the one or more social groupings corresponds to one or more particular user profile attributes; identifying, for a particular user account, one or more of the activity groupings having a social grouping corresponding to user profile attributes of the particular user account; and modifying one or more user experience features of the system based on the identified one or more activity groupings.

Profiling user activity to achieve social and governance objectives

A dynamic, personality-driven user experience that includes receiving, from a user account, a request to access a system interface for a system; identifying, based on personality indicators for the user account, a user account personality from a plurality of user account personalities, wherein each of the personality indicators is associated with at least one of the plurality of user account personalities; reconfiguring the system interface based on the identified user account personality; and granting, to the user account, access to the reconfigured system interface, including presenting the reconfigured system interface to a user of the user account.

Dynamic, personality-driven user experience

Data associated with a first set of managed flash storage devices of a cloud-based storage system is provided as an input to a machine learning model executed by a processing device that identifies one or more characteristics of the first set of managed flash storage devices from the data. A type of change associated with a second set of managed flash storage devices is determined by the machine learning model based on a comparison of the one or more characteristics of the first set of managed flash storage devices and one or more characteristics of the second set of managed flash storage devices. The type of change associated with the second set of managed flash storage devices is provided to a cloud services provider of the cloud-based storage system.

Utilizing machine learning for processing of managed flash storage devices

Non-disruptively moving a storage fleet control plane, including deploying, on an edge device, one or more agents that are managed by a control plane residing in a cloud computing environment; mediating one or more API requests, generated by the control plane, directed to the one or more agents on the edge device; and migrating, in response to a first condition, the control plane to the edge device.

Non-disruptively moving a storage fleet control plane

Context driven user interfaces for storage systems including receiving, from a user account, a request to access a system interface for a system; identifying at least one critical system characteristic that describes a current aspect of the system; reconfiguring the system interface based on the at least one critical system characteristic; and presenting the reconfigured system interface to a user of the user account.

Context driven user interfaces for storage systems

Providing data management as-a-service, including: presenting one or more available data services to a user; receiving a selection of one or more selected data services; and applying, in dependence upon the one or more selected data services, one or more data services policies to a dataset associated with the user.

Providing data management as-a-service

Rebalancing in a fleet of storage systems using data science including generating, by the cloud-based rebalancing system, a plurality of workload migration scenarios to address a detected workload imbalance among a plurality of workloads in a fleet of storage systems; determining, by the cloud-based rebalancing system, a plurality of movement vectors for each workload migration scenario, wherein each of the plurality of movement vectors describes a consideration factor for migrating a workload of the plurality of workloads within the fleet of storage systems; and generating, by the cloud-based rebalancing system, at least one rebalancing proposal based on the plurality of movement vectors for each workload migration scenario.

Rebalancing in a fleet of storage systems using data science

Data associated with a first storage media of a cloud-based storage system is provided as an input to a machine learning model executed by a processing device that identifies one or more characteristics of the first storage media from the data. A type of change associated with the second storage media is determined by the machine learning model based on a comparison of the one or more characteristics of the first storage media and one or more characteristics of a second storage media. The type of change associated with the second storage media is provided to a cloud services provider of the cloud-based storage system.

Optimized machine learning telemetry processing for a cloud based storage system

Providing storage tailored for a storage consuming application, including: identifying, for an application that utilizes storage resources within a cloud-based storage system, one or more storage performance characteristics associated with the application; comparing the storage performance characteristics of the application that were identified with storage performance characteristics of storage resources of one or more cloud-based storage systems; and selecting, based on the comparing, one or more storage resources within the one or more cloud-based storage systems to provide storage services to the application.

Providing storage tailored for a storage consuming application

Assessing protection for storage resources, including: identifying a set of active data protection features for one or more storage resources; generating a data protection assessment based on the set of active data protection features; and reporting the data protection assessment.

Assessing protection for storage resources

Role enforcement for storage-as-a-service including managing, for a storage system, a plurality of roles including a storage consumer role and a storage provider role, wherein the storage consumer role is associated with data management instructions that are enabled for the storage consumer role and disabled for the storage provider role, and wherein the storage provider role is associated with storage management instructions that are enabled for the storage provider role and disabled for the storage consumer role; servicing a data management instruction from a first client associated with storage consumer role, wherein the data management instruction is an instruction to manipulate data on the storage system; and servicing a storage management instruction from a second client associated the storage provider role, wherein the storage management instruction is an instruction to manage the storage system.

Role enforcement for storage-as-a-service

Data associated with storage media utilized by one or more storage systems is received. The data is provided as an input to a machine learning model executed by a processing device. The machine learning model identifies one or more deterministic characteristics from the data. The one or more deterministic characteristics associated with the storage media are received from the machine learning model. A data structure comprising the one or more deterministic characteristics is generated for use in a telemetry process to qualify types of storage media.

Machine learning model for storage system

Adaptive storage processing for storage-as-a-service, including detecting, by a cloud-based monitoring system, a storage system state for a storage system by monitoring the storage system in real-time remotely via a network; selecting, by the cloud-based monitoring system based on the storage system state, an entry in a tunables repository, wherein the entry in the tunables repository comprises a tunable parameter for the storage system state; accessing, by the cloud-based monitoring system via the network, a gateway for the storage system; and modifying, by the cloud-based monitoring system via the gateway, the tunable on the storage system based on the tunable parameter for the storage system state.

Adaptive storage processing for storage-as-a-service

Utilizing machine learning to streamline telemetry processing of storage media

Providing application-specific storage by a cloud-based storage system, including: identifying, for an application that utilizes resources within the cloud-based storage system, one or more characteristics associated with the application; and selecting, in dependence upon the one or more characteristics associated with the application and characteristics of resources within the cloud-based storage system, one or more resources within the cloud-based storage system to support the execution of the application, wherein at least a portion of a dataset associated with the application is stored as blocks within block storage resources in the cloud-based storage system and also stored as objects within object storage resources in the cloud-based storage system.

Growing community of inventors

Santa Clara, CA, United States of America

Prakash Darji