Pure Storage, Inc.

Naveen Revanna

Kshithij Iyer

Aditya Dani

Luis Pablo Pabón

Piyush Nimbalkar

Prabir Paul

Lakshmi Narasimhan Sundararajan

Paul Theunis

Harsh Desai

Neelesh Thakur

Madanagopal Arunachalam

Priyanshu Pandey

Dhruv Bhatnagar

An example method for automated elastic resource management of a container system by a storage system comprises providing, by a container storage management system, a volume for use by a containerized application of a container system, the volume deployed on a node of the container system; analyzing, by the container storage management system, input/output (I/O) operations to the volume based on historical I/O operations; and directing, by the container storage management system and based on the analyzing the I/O operations based on the historical I/O operations, the container system to adjust a capacity of a resource associated with the node.

Automated elastic resource management of a container system by a distributed storage system

An example method for detecting and remediating anomalies in a container system by a storage system comprises detecting, by a container storage management system, a change in resources utilized on a volume of the container system by an application of the container system; determining, by the container storage management system and in response to the detecting, whether the change in resources utilized is anomalous for the application; and performing, by the container storage management system and based on the determining, an action associated with the application.

Detecting and remediating anomalies of a container system by a distributed storage system

An example method for analyzing logs for root causes of errors in a cloud environment comprises: detecting an error in a component deployed within a cloud environment; providing, based on the detecting the error, log data associated with the component to a root cause analysis machine learning model; determining, based on an output of the machine learning model, a corrective measure configured to address the error; and performing a remedial action associated with the corrective measure.

Analyzing logs for root causes of errors in a cloud environment

An example method for prioritizing deployment of nodes in a cluster comprises: determining, at a first time and by a first node of a plurality of nodes in a cluster of a distributed storage system, that a second node having a higher priority than the first node is in a startup phase of the second node; determining, at a second time subsequent to the first time and by the first node, that the second node has completed the startup phase of the second node; and initiating, based on the determining that the second node has completed the startup phase and by the first node, a startup phase of the first node.

Prioritized deployment of nodes in a distributed storage system

Clients that fail to access a resource may compute backoff times for waiting before retrying to access the resource. The clients may reference a sequence of time windows where the time windows may have increasing numbers of time slots. The clients may select respective time slots in a first time window. If the clients again fail to access the resource, then the time slots selected by the clients may at least partly determine which time slots the clients select in a next (second) time window. The time slot selections in the second time window may be at least partly randomly selected. A client may randomly select a time slot in the second time window from among a proper subset of time slots in the second time window. The proper subset may be based on which time slot the client selected in the first time window.

Adaptive distributed backoff scheme

An example method for dynamic determination of locks for prioritized deployment in a distributed storage system comprises providing, by a storage management system, N locks to a cluster of a distributed storage system, the cluster including a plurality of nodes and the N locks allowing N nodes of the plurality of nodes to each initiate a startup phase during a first time window, wherein N is a number greater than zero; tracking, by the storage management system, application programming interface (API) calls made by the N nodes to a platform of the cluster during the first time window; and providing, by the storage management system based on the tracking, M locks to the cluster, the M locks allowing M nodes of the plurality of nodes to each initiate a startup phase during a second time window subsequent to the first time window, wherein M is a number greater than zero.

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Campbell, CA, United States of America

Naveen Revanna