Veritas Technologies LLC

Symantec Corporation

Symantec Operating Corporation

Aaron Christensen

Kirk L Searls

Joshua David Kruck

Ryan Schroeder

Guido Westenberg

Girish Sureshrao Jorapurkar

William Joseph Browning

Samuel Pierson

Xianbo Zhang

Weibao Wu

Sinh Nguyen

Nitin Suresh Supekar

Steve A Vranyes

Gaurav Khanna

Prabhu Damodharan

Erica Antony

Jed Gresham

Sreekanth Vadapalli

Jatheen Anand

Michael W Boldt

The disclosed computer-implemented method for using dynamic templates to create application containers may include (i) identifying an application that is to be deployed in a container, (ii) creating a dynamic template that comprises at least one variable parameter and that defines at least a portion of an operating environment of the container (iii) generating a value of the variable parameter during deployment of the application, (iv) processing the dynamic template to create a configuration file that comprises the value of the variable parameter, and (v) triggering a container initialization system to create, based on the configuration file, the container such that the container isolates a user space of the application from other software on a host system while sharing a kernel space with the other software. Various other methods systems, and computer-readable media are also disclosed.

Systems and methods for using dynamic templates to create application containers

The disclosed computer-implemented method for preparing email databases for analysis may include (1) identifying an email database that stores a plurality of emails in a plurality of tables that are formatted to be managed by a specific email application, (2) using a component of the specific email application to retrieve the plurality of emails from the database, (3) creating a denormalized dataset for the plurality of emails by combining email data from at least one table from the plurality of tables with email data from at least one other table from the plurality of tables, and (4) exporting at least a portion of the data from the denormalized dataset into at least one file in an interoperable format that is capable of being read by a plurality of applications. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for preparing email databases for analysis

The disclosed computer-implemented method for updating email analytics databases may include (1) identifying an email database with a native format and an email analytics database that stores a copy of data in the email database in an analytics-friendly format that is denormalized relative to the native format of the email database, (2) capturing a log file comprising information that is about at least one recent change to the email database and that is formatted using the native format of the email database, (3) extracting the information about the recent change to the email database from the log file by transforming the information from the native format of the email database into the analytics-friendly format, and (4) updating the email analytics database to reflect the recent change to the email database by using the extracted information. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for updating email analytics databases

The disclosed computer-implemented method for automatically linking data analytics to storage may include (1) identifying a request to provision storage for a data analytics task, (2) collecting information relating to the data analytics task, the information comprising at least one of a data type of the data being used as input for the data analytics task and a characteristic of the data analytics task, (3) using a self-service provisioning tool to automatically compute, based on the collected information, a suggested type and size of data storage for the data analytics task, and (4) automatically provisioning data storage for the data analytics task based on the suggested type and size. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for automatically linking data analytics to storage

The disclosed computer-implemented method for building an extract, transform, load pipeline may include (i) identifying a plurality of ETL resources available to a user, (ii) categorizing each of the plurality of ETL resources based on at least one characteristic, (iii) provisioning the plurality of ETL resources for use with containers, (iv) presenting a user environment to the user, the user environment including a plurality of container images corresponding to available ETL resources, (v) receiving, from the user, a selection of at least one container image of the plurality of container images, and (vi) running at least one container from the at least one container image, the at least one container utilizing two or more ETL resources of the plurality of ETL resources. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for building an extract, transform, load pipeline

The disclosed computer-implemented method for creating a data volume from within a software container and initializing the data volume with data may include (i) running, from a volume creation container image, a volume creation container that isolates user space of the volume creation container from other processes while sharing kernel space with the other processes, (ii) creating a new data volume from within the volume creation container, and (iii) executing, from within the volume creation container, a run command to run a data initialization container from the volume creation container image and mount the new data volume to a target mount point in the data initialization container, the data initialization container executing a data initialization command to copy data to the new data volume. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for creating a data volume from within a software container and initializing the data volume with data

The disclosed computer-implemented method for using dynamic templates to create application containers may include (i) identifying an application that is to be deployed in a container, (ii) creating a dynamic template that comprises at least one variable parameter and that defines at least a portion of an operating environment of the container, (iii) generating a value of the variable parameter during deployment of the application, (iv) processing the dynamic template to create a configuration file that comprises the value of the variable parameter, and (v) triggering a container initialization system to create, based on the configuration file, the container such that the container isolates a user space of the application from other software on a host system while sharing a kernel space with the other software. Various other methods, systems, and computer-readable media are also disclosed.

The disclosed computer-implemented method for completing sets of computing tasks may include (i) defining a set of computing tasks to be performed in a specified order, (ii) configuring a set of containers such that each container within the set of containers is configured to perform a task within the set of computing tasks, where each container isolates user space of a deployment system from other software on the deployment system while sharing kernel space with the other software, (iii) receiving a request to initiate the set of computing tasks, and (iv) completing, in response to the request, the set of computing tasks by launching the set of containers in the specified order. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for completing sets of computing tasks

VMs are booted in a big data framework within the context of a cluster of computing and storage devices. The big data framework comprises a distributed, location aware file system and a cluster resource manager that assigns computing resources. VM images are stored as data in the distributed file system. Computing resources and hosts are allocated to specific VMs. The allocated hosts are within given levels of proximity to target data. VMs are booted and run on the hosts, and applications are run on the VMs, processing target data in the distributed file system. Prior to booting a given VM, a stored image can be converted between formats. This enables dynamically determining the VM format at boot time, based on what is supported by the hypervisor available on the target host.

Booting virtual machine instances in a distributed data processing architecture

The disclosed computer-implemented method for performing storage location virtualization may include (i) identifying a volume storage location for an operating-system-level virtualization container that isolates an instance of user space, the volume storage location providing a working space for data accessed during the existence of the operating-system-level virtualization container, (ii) mapping, through an automated driver that performs location virtualization, the volume storage location to multiple distinct source locations outside of the operating-system-level virtualization container, rather than a single source location, and (iii) propagating, through the automated driver, changes to the data within the working space of the volume storage location during the existence of the operating-system-level virtualization container to the respective multiple distinct source locations outside of the operating-system-level virtualization container to preserve the changes. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for performing storage location virtualization

The disclosed computer-implemented method for providing access to data sets owned by different entities may include (1) providing a data marketplace platform that allows a plurality of data set owners to each list their own data sets for licensing to any of a plurality of prospective data set license buyers, (2) receiving, at the data marketplace platform, a data set owned by a data set owner, (3) creating a sale listing for the data set, (4) displaying, on the data marketplace platform, the sale listing for the data set to at least one prospective data set license buyer, (5) receiving, from the prospective data set license buyer, an order to license the data set, and (6) providing, via the data marketplace platform, access for the prospective data set license buyer to the data set. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for providing access to data sets owned by different entities

The disclosed computer-implemented method for distributing configuration templates with application containers may include (i) identifying an application to be deployed in an application container, (ii) maintaining a configuration template comprising at least one configuration setting for the application container and code that transforms the configuration template into a configuration file during deployment of the application, (iii) packaging the configuration template, the code that transforms the configuration template, and the application in a deployment container image, (iv) deploying the deployment container image to a host computing system that comprises a container engine that creates an instance of the deployment container from the deployment container image, and (v) triggering, by the instance of the deployment container, the container engine to use the configuration file generated from the configuration template to configure the application container with the configuration setting. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for distributing configuration templates with application containers

Trust relationships in an online service system are established at a domain level, and propagated to components of domains as they attempt cross domain communication. In attempting to communicate across domains, a first component in a first domain attempts to validate a certificate of a second component in a second domain. Where the attempt to validate the certificate indicates that a trust relationship does not exist between the first component and the second domain, the first component determines whether a domain level trust relationship exists between the two domains. The first component propagates the trust status between the first and second domains to itself. If there is an existing trust relationship between the first and second domains, the first component validates the certificate of the second component in response. The second component executes the same process to complete the connection.

Just in time trust establishment and propagation

Providing authentication of users accessing an NFS shared file system. A shared secret is used as a component of the mount point used to access the NFS share. Upon receiving a request to access to the data in the NFS share, the process creates at least one cryptographic value and then creates a path to the mount point which incorporates the cryptographic value. The process then creates the mount point at the path, e.g., /PATH:k1, where k1 is the cryptographic value. Creation of the mount point is preferably performed using NFS protocol semantics, without requiring any changes to the NFS protocol semantics. A second cryptographic value, k2, may be used for unmounting the mount point.

Controlling access to an NFS share

A method for maintaining applications may include: (1) receiving a request to recover a first application, (2) identifying a first production topology of the first application that identifies a set of resources upon which the application depends, (3) maintaining a template for transforming the first production topology of the first application into a first recovery topology for the first application, the template comprising information for mapping the first production topology to the first recovery topology, (4) applying the template to the first production topology at a first point in time to create the first recovery topology, and (5) recovering the first application to a first computing system using the first recovery topology. Various other methods, systems, and computer-readable media are also disclosed herein.

Growing community of inventors

Apple Valley, MN, United States of America

Aaron Christensen