Dell Products Inc.

Emc IP Holding Company LLC

Dell Technologies, Inc.

Jennifer M Minarik

Mark D Malamut

Brian E Freeman

Reut Kovetz

Sumedh Wasudeo Sathaye

Kelly L Lisai

Patrick East

Jehuda Shemer

Ching-Yun Chao

Nicole Reineke

Arthur F Lent

Qi Jin

Yi Fang

Ranjit Kollu

Yidong Wang

David Scott Thompson

Jonathon Cwik

Jacob R Hutcheson

Mark Malamut

Kevin D Cheng

A system can identify an attribute-based access control policy that comprises a pair comprising a permission policy and a condition policy that is associated with performing an operation on a computing resource. The system can identify that the attribute-based access control policy corresponds to a role policy that is associated with the account. The system can determine whether the account and the role policy satisfy the attribute-based access control policy with respect to the operation, wherein the determining comprises evaluating whether the computing resource and a required permission of the permission policy is declared in the role policy, and evaluating whether the account and the role policy satisfy the condition policy evaluates to true based on attributes of the account and attributes of the computing resource. The system can, in response to determining that the account satisfy the permission policy and the condition policy, perform the operation on the computing resource.

Attribute-based access control using scoped roles and conditioned permissions dynamic policies

Selecting user access policies for a new system, by collecting user, access policy, and resource metadata for a plurality of other users storing data dictated by one or more access restriction policies. The collected metadata is anonymized with respect to personal identifying information, and is stored in an anonymized analytics database. The system receives specific user, access policy and resource metadata for the new system from a specific user, and matches the received specific user metadata to the collected metadata to identify an optimum access policy of the one or more access policies based on the assets and access restriction requirements of the new system. The new system is then configured with the identified optimum access policy as an initial configuration.

Automatically creating data protection roles using anonymized analytics

A system that automatically determines ideal temporary backup targets (TBTs) to store and restore backup data. For multiple TBTs, backup data is saved with location information to restore incremental backup data from these multiple targets. Hashes of each file are stored in a Merkle tree to enable data restore workflows and minimizing an amount of storage required to hold backup data. The lowest tree level comprises hashes of the backup data chunks, and higher levels contain hashes of the next lower levels. A backup target location value is added to non-data chunk hash records in the higher levels to identify where any piece of backup data is located so that restore operations can occur when the backup data is spread across multiple backup targets.

Restoring from a temporary backup target in an intelligent destination target selection system for remote backups

Embodiments are described for a system that automatically determines the ideal backup target to send backup data. Temporary backup targets are automatically created to handle backups for mobile data assets. A backup agent sends incremental backups to any temporary backup target (TBT), which are later consolidated with primary backup target (PBT) data. To facilitate data backup to a TBT and restore operations from data located on a PBT or TBT, a Backup Location Catalog (BLC) is created for each asset to reside on the asset. A Change Record Catalog (CRC) is created for each TBT backup per asset and each CRC resides on the asset until the associated data is consolidated back to the PBT. Assets thus have access to all backups, including the data on the TBT(s) before that data is consolidated back to the PBT.

Intelligent destination target selection for remote backups with awareness of temporary backup target for data restores

Embodiments for generating user customized alert notifications for application operations and activities based on monitored performance metrics. Key performance indicators for the application and user behavior are defined, and a monitor process collects behavior statistics of the application for each user with respect to data assets for each of the key performance indicators. Anomaly detection policies are provided to define anomalous behavior of the users with respect to data assets of the computer network. An anomaly detection process detects anomalous user behavior and an alert notification is sent to administrative or security personnel upon each detected instance of abnormal user behavior. The alert notification rules are defined by the user based on operation severity, asset type, operation, and defined metrics to tailor and minimize the number of alerts sent to the user.

Intelligent alert customization in a backup and recovery activity monitoring system

Embodiments for a data protection method of prioritizing data assets for backup operations. A base priority of data assets operated on by the backup system is first determined as defined by certain characteristics, along a defined scale. The process then prioritizes certain data assets using priority tagging, grouping factors, and metadata modifiers to generate an interim net priority. If any assets have the same prioritization value, other asset attributes are used to further prioritize any tied assets. The process then performs a priority response action, such as notifying the user (normally or urgently) based on the asset prioritization. The backup/restore operations are then performed on the data assets in an order based on the final prioritization values.

Enhanced prioritization on data protection assets

An automated system for implementing visual testing of a Graphical User Interface (GUI) of an implemented product is provided. A codified user experience design is used to automatically create precompiled code. The precompiled code is executed to generate a GUI based on the intended codified user experience design. The implemented product code is executed to generate a GUI of the implemented product code. Screenshots of the GUIs are compared to perform automatic visual testing of the implemented product code from the codified user experience design.

Visual testing of implemented product code from a codified user experience design

Embodiments are described for a system that automatically determines the ideal backup target for the backup agent to send its backup data. The system will automatically create and destroy temporary backup targets in order to handle the current backup traffic and provide the optimal throughput based on speed of the backup agent and reliability of the storage target. The backup agent is able to send incremental backups to any temporary backup target, which are later consolidated on the storage target after the agent has disconnected from the system. The final storage target may be an original primary storage target for the data asset or one of the temporary storage targets.

Intelligent destination target selection for remote backups

Designers create user experience designs using external design systems. An Experience Design Codification and Management System (EDCMS) retrieves a user experience definition based on the user experience design from the external design system, and generates a comprehensive user experience specification from the user experience definition. Part of the comprehensive user experience specification includes JSON, XML, or YAML code created based on the user experience definition. The EDCMS then packages and encodes the comprehensive user experience specification to create a codified user experience from the comprehensive user experience specification. The codified user experience is then versioned and digitally signed, and the versioned and signed codified user experience is stored in a user experience design repository.

Method and apparatus for codifying user experience designs and managing the codified user experience designs

Embodiments for a system and method of selecting data protection policies for a new system, by collecting user, policy, and asset metadata for a plurality of other users storing data dictated by one or more protection policies. The collected metadata is anonymized with respect to personal identifying information, and is stored in an anonymized analytics database. The system receives specific user, policy and asset metadata for the new system from a specific user, and matches the received specific user metadata to the collected metadata to identify an optimum protection policy of the one or more protection policies based on the assets and protection requirements of the new system. The new system is then configured with the identified optimum protection policy as an initial configuration.

Automatically assigning data protection policies using anonymized analytics

Embodiments for a data protection method of grouping assets for protection policy assignment based on asset metadata by defining a set of metrics characterizing each asset in the system and comparing each metric of an asset with corresponding metrics of other asset groups each containing one or more other assets. A unique protection policy is assigned to each group for application to each asset within a respective group. An overall affinity percentage of the metrics of asset with the corresponding metrics of each group is determined, and the asset is automatically grouped into the group based with the highest overall affinity percentage. The user is prompted to confirm the automatic grouping or to select a different group for assigning to the asset.

Leveraging asset metadata for policy assignment

An automated template and logic generator retrieves a codified user experience design having persona and outcome metadata and a finite state machine. An aggregation engine walks the finite state machine, identifies objects in each state, and selects components that correspond with the objects from a component library. Actions associated with the components are identified, and used to identify API calls that are required to be made to implement the actions. Corresponding APIs are aggregated to create a back-end for front-end (BFF) layer for the codified user experience definition. A pattern is created for a front-end layer for the codified user experience design based on the FSM. A code generator builds components to the objects of the codified user experience design to generate precompiled code, which is packaged with a node.js layer to allow local runtime. The generated precompiled code is versioned and placed in the user experience design repository.

Automatic template and logic generation from a codified user experience design

A partially completed user experience design can be completed by selecting a previous codified user experience design, and determining portions of a Finite State Machine of the previous codified user experience design that should be added to a FSM of the partially completed user experience design. This enables use of a previous codified user experience designs to complete the FSM of a partially completed user experience design. Previously codified user experience designs can also be used as the basis for creation of new user experience designs. A previously codified user experience design is selected, and newly created portions of the FSM that are to be added to the FSM of the previously codified user experience design are added to the FSM of the previously codified user experience design. In both instances, components and patterns of the user experience design are automatically substituted to ensure uniformity within the created design.

Completing partially constructed designs and creating new designs based on existing designs

Embodiments for a system and method of monitoring performance metrics of a computer network, by defining key performance indicators for the user behavior in the computer network, collecting behavior statistics for each user for each of the key performance indicators, and providing one or more anomaly detection policies to define anomalous behavior of the users with respect to data assets of the computer network. An anomaly detection policy is applied to the collected behavior statistics to detect anomalous user behavior and an alert notification is sent to administrative or security personnel upon each detected instance of abnormal user behavior.

Intelligent monitoring of backup, recovery and anomalous user activity in data storage systems

Codified graphical user experience design includes finite state machine (FSM) metadata, logic describing the graphical aspects of the user experience design, and logic describing states of the user experience design and transitions between states. A GUI to API/CLI translator uses the codified graphical user experience design to automatically create corresponding CLI packages and API playbooks. The translator walks the FSM. For each visited state, a state based functional intermediate code package is created including the one or more API call basic blocks associated with API calls to be implemented at that state, and parameter definitions for each API call at that state. For each state transition, conditional intermediate code is created and attached to the API call basic blocks. The state based functional intermediate code packages with attached conditional intermediate code are used to create a CLI package or an API playbook based on the codified graphical user experience design.

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Zionsville, IN, United States of America

Jennifer M Minarik