Emc IP Holding Company LLC

Dell Products Inc.

Emc Corporation

Mark D Malamut

Adam E Brenner

Arun Murti

Jennifer M Minarik

Brian E Freeman

Scott Auchmoody

Jeffrey Norton

Joey C Lei

Stephen Smaldone

Lev Ayzenberg

Jennifer Starling

Erik Shawn Hansen

Vijay Anand

Sharon Vitek

Jehuda Shemer

Junxu Li

Grant R Wallace

Frederick Douglis

Vladimir Mandic

Wei Wang

Christopher J Hackett

Maxim Drobachevsky

Mark Twomey

Kevin D Cheng

Embodiments are described for a method and system of applying data protection software mechanisms to network devices to auto-discover the networking equipment, save changes from memory (TCAM) to local storage, backup changes to protection storage, provide auditing and tracking history of changes, and provide the ability to deploy test/development copies of changes using software defined networking techniques. A coordinator protects network devices organized into a plurality of partitions by creating a backup of each network device, pushing backup policies to individual data protection units for the network devices within each partition to provide a consistent-state backup of the network devices, and backing up the configuration changes of the network devices to a protection storage device.

Coordinated data protection for multiple networking devices

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 are described for a method and system of applying data protection software mechanisms to network devices to auto-discover the networking equipment, save changes from memory (TCAM) to local storage, backup changes to protection storage, provide auditing and tracking history of changes, and provide the ability to deploy test/development copies of changes using software defined networking techniques. Embodiments include an efficient visual mapping aspect provided through a GUI to display the topography and backup/protection configuration of network devices in a system.

Visual mapping of protection status for networking devices

One example method includes receiving from a node, in an HSAN that includes multiple nodes, an ADD_DATA request to add an entry to a distributed ledger of the HSAN, the request comprising a user ID that identifies the node, a hash of a data segment, and a storage location of the data segment at the node, performing a challenge-and-response process with the node to verify that the node has a copy of the data that was the subject of the entry, making a determination that a replication factor X has not been met, and adding the entry to the distributed ledger upon successful conclusion of the challenge-and-response process.

Hyper-scale P2P deduplicated storage system using a distributed ledger

Embodiments automate the timely remediation of issues by matching failed operations to a script or set of scripts that is configured to fix the problem. A method detects a backup operation alert message sent from a data manager or storage system that encodes one or more attributes affected by the backup operation, wherein the attributes are weighted to reflect a percentage importance of the a corresponding script to fix the problem. A script library is scanned to identify scripts that match the alert message. A component generates a score for each script, where the score factors the relevance of the script to the backup operation based on the weighted attributes. Scores for each single script each possible combination of multiple scripts are computed and compared, and the script or script combination with the highest score is executed by the self-healing process.

Self-healing data protection system matching system attributes to relevant scripts using multiple scripts

One example method includes defining object groups by classifying each object in a backup saveset based on respective object types of the objects such that all objects in an object group are the same object type, assigning a different respective storage media type to each of the object groups, storing each object group at a respective storage target, representing each object group with a respective Merkle tree that includes a base hash, and mapping each base hash to the storage target where the object group associated with the Merkle tree that includes the base hash is stored.

Automatically determining optimal storage medium based on source data characteristics

Embodiments for a method performing data migration such as backups and restores in a network by identifying characteristics of data in a data saveset to separate the data into defined types based on respective characteristics, assigning a data label to each defined type by receiving user selection or automatically merging or selecting a priority label, from among many labels associated with a file, defining migration rules for each data label, discovering assigned labels during a migration operation; and applying respective migration rules to labeled data in the data saveset. The migration rules can dictate storage location, access rights, replication periods, retention periods, and similar parameters.

Multiple data labels within a backup system

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 are described for preventing undesired data deletion on protection storage by using delete restrictions. A delete restriction component prevents data from being staged for permanent erasure, such as by preventing files from being moved to a trash folder. A well-known process (WKP), such as a backup or migration operation that is known to request data expiration, is used to validate data deletions. The WKP identifies data that is no longer to be retained. The WKP uses a well-known process expiration list (WEL) that holds hashes of the data. A bucket confirmation count indicates candidates for data deletion. Separately, Garbage Collection maintains another list of hashes that are deletion candidates. A separate process finds common hashes between the WKP and GC hashes, and then removes the data pointed to by the common hashes.

Preventing attacks on protection storage using delete restriction

Embodiments automate the timely remediation of issues by matching failed operations to a script that is configured to fix the problem or problems without causing additional side effects, thus minimizing unavailability of services and loss of data. A method detects a backup operation alert message sent from a data manager or storage system that encodes one or more attributes affected by the backup operation, wherein the attributes are weighted to reflect a percentage importance of the a corresponding script to fix the problem. A script library is scanned to identify scripts that match the alert message. A component generates a score for each script, where the score factors the relevance of the script to the backup operation based on the weighted attributes, and executes the script with the highest score, as the script most likely to fix the issue causing the alert message.

Self-healing data protection system matching system attributes to relevant scripts using weighted attributes

Embodiments help timely remediation of issues in a data protection system by automatically evaluating scripts configured to address the issues. The system has a bounded list of possible attributes that are deemed important by system administrators. Each attribute is assigned a System Impact Score (SIS) along a defined scale. A self-healing processing component monitors the state of each attribute over time. The scripts are evaluated through repeated execution and use of the attribute monitoring to determine which attributes are affected by a script. Weights are assigned to each attribute affected by a script to aid in the selection of scripts most likely to remediate an actionable issue. Regularly performing script evaluation and attribute weighting allows for updating of scripts with an accurate list of attributes to overcome problems associated with manual updates.

Self-healing data protection system automatically determining attributes for matching to relevant scripts

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

Tracking changes to a document by defining a document record having a unique document record and comprising an index and a file name of the document, and defining a backup record for the document in a series of backups, which includes a timestamp for each backup, and a bitmask for the document. The bitmask has a single bit position for each document in the container which is set to a first binary value to indicate that the corresponding document is unchanged and a second binary value to indicate whether the document is changed or deleted. A primary query is received and resolved for the document by analyzing the document record to find the file name. A secondary query using the document record ID is resolved to find all tracked versions of the document, and the results are returned to the user in the form of a version history list.

File lifetime tracking for cloud-based object stores

One example method includes receiving, from an entity, a proposed entry for a ledger, where the ledger is shared and accessible by multiple users and includes a whitelist and a blacklist, determining, or assigning, a credibility score and rate limiter value for the entity, comparing the credibility score and rate limiter value with respective credibility score and rate limiter value thresholds, determining that the credibility score and rate limiter value meet or exceed the respective credibility score and rate limiter value thresholds, and submitting the proposed entry to the ledger.

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Aliso Viejo, CA, United States of America

Mark D Malamut