Pure Storage, Inc.

Farhan Abrol

Neil A Vachharajani

Mark L McAuliffe

Ronald S Karr

Constantine P Sapuntzakis

Timothy W Brennan

Lei Zhou

Apoorva Bansal

Yi-Chin Wu

Robert Barker, Jr

Chadd Kenney

Mark McAULIFFE

Neil Buda Vachharajani

Gregory McNutt

Weizhong Hua

Ronald Ekins

Isa Pakatci

Daniel Wilson

Loïc Magnin

James Gallegos

Savas Nicolaides

Mark Mcauliffe

Mitchell Wells

Volodymyr Kovalenko

Kapil Kanwar

Staging data on a storage element integrating fast durable storage and bulk durable storage, including: receiving, at a storage element integrating fast durable storage and bulk durable storage, a data storage operation from a host computer; storing data corresponding to the data storage operation within fast durable storage in accordance with a first data resiliency technique; and responsive to detecting a condition for transferring data between fast durable storage and bulk durable storage, transferring the data from fast durable storage to bulk durable storage in accordance with a second data resiliency technique.

Tiering valid data after a disaster recovery operation

Ensuring resiliency to storage device failures in a storage system, including: determining a number of storage device failures within a particular write group that are to be tolerated by the storage system; for a plurality of datasets stored within the storage system, writing each dataset to at least a predetermined number of storage devices within the particular write group, wherein the predetermined number of storage devices is greater than the number of storage device failures within the particular write group that are to be tolerated by the storage system; and responsive to recovering from a system interruption: determining a number of readable storage devices that contain a copy of the dataset; and if the number of readable storage devices that contain a copy of the dataset is not greater than the number of failures that are to be tolerated, writing the dataset to one or more additional storage devices.

Using mirrored copies for data availability

Migrating workloads to a preferred environment, including: predicting, for each of a plurality of environments, a performance load on each of a plurality of environments that would result from placing one or more of a plurality of workloads on the environment; determining a preferred environment for each of the plurality of workloads by determining a placement of each of the plurality of workloads that results in a best fit for the plurality of workloads; and deploying each of the plurality of workloads in the corresponding preferred environment.

Migrating workloads to a preferred environment

Migrating workloads among execution environments including storage systems includes: selecting a target execution environment for supporting a workload and migrating the workload to the target execution environment utilizing active disaster recovery. Migrating the workload can include: assigning storage resources of the workload to a first pod; linking for replication, the first pod to a second pod of the target execution environment; and replicating the storage resources of the workload to the second pod of the target execution environment.

Migrating workloads using active disaster recovery

Workload placement based on carbon emissions, including: calculating, for each execution environment of a plurality of execution environments, a carbon emission cost associated with a workload; selecting, based on each carbon emission cost for the plurality of execution environments, a target execution environment; and executing the workload on the target execution environment.

Workload placement based on carbon emissions

Utilizing volume-level policies in a storage system, including: identifying, for each of a plurality of volumes, one or more characteristics associated with usage of the volume; associating, using the one or more characteristics associated with the usage of the volume, one or more storage system policies with the volume; and configuring, using the one or more storage system policies that are associated with the volume, the storage system.

Utilizing volume-level policies in a storage system

Recommending changes to a storage system, including: identifying, using predicted characteristics of one or more workloads executing on the storage system, one or more configuration changes to the storage system that would improve the operation of the storage system; selecting, from the one or more configuration changes to the storage system that would improve the operation of the storage system, a preferred configuration change; and recommending the preferred configuration change.

Recommending changes to a storage system

Managing an application's resource stack, including: detecting, in dependence upon one or more storage system metrics, an occurrence of a storage system performance anomaly; and responsive to detecting the storage system performance anomaly, identifying, in dependence upon codified relationships between one or more storage system metrics and one or more elements in the application stack that are external to the storage system, a root cause of the storage system performance anomaly.

Managing an application's resource stack

Mirroring data to survive storage device failures

Identifying valid data after a storage system recovery

Migrating workloads between a plurality of execution environments, including: identifying, in dependence upon on characteristics of a workload, one or more execution environments that can support the workload; determining, for each execution environment, costs associated with supporting the workload on the execution environment; selecting, in dependence upon the costs associated with supporting the workload on each the execution environments, a target execution environment for supporting the workload; and executing the workload on the target execution environment.

Cost optimized workload placement

Preserving tolerance to storage device failures in a storage system

Providing workload mobility between disparate execution environments, including: identifying, by a workload migration module, a workload executing in a first environment, wherein the workload includes a software application that is being executed and data that is accessed by the software application; identifying, by a workload migration module, a second environment for servicing the workload; and deploying, by a workload migration module, the workload in the second environment.

Workload mobility between disparate execution environments

A stream of events is received, where individual events within the stream of events are received out-of-order. The events from the stream of events are ordered. Whether a predetermined collection of events have occurred within a predetermined period of time is determined. Responsive to determining that the predetermined collection of events have occurred within the predetermined period of time, an action associated with the predetermined sequence of events is initiated.

Event identification with out-of-order reporting in a cloud-based environment

Presenting workload performance in a storage system, including: receiving, via a user interface, information describing a potential change to an execution environment of the storage system; and displaying, via the user interface and in dependence upon a load model and predicted characteristics of one or more workloads executing on the storage system, predicted performance load on the storage system that would result from implementing the potential change.

Growing community of inventors

San Francisco, CA, United States of America

Farhan Abrol