Emc IP Holding Company LLC

Dell Products Inc.

Adam E Brenner

Mark D Malamut

Arun Murti

Jehuda Shemer

Steven Sadhwani

Valerie Lotosh

Erez Sharvit

Jennifer Starling

Joey C Lei

Upanshu Singhal

Lev Ayzenberg

Jeffrey Norton

Vijay Anand

Sharon Vitek

Junxu Li

Chris E Rowen

Erik Shawn Hansen

Jing Yu

Ming Zhang

Mengze Liao

Scott Auchmoody

Lihui Su

Yongsheng Guo

Yubing Zhang

Chandra Prakash

Cherami Min Liu

Maxim Drobachevsky

Scott Ming Zhang

Aaditya Rakesh

Jerry Jourdain

Providing content-based sensitivity classification to content data in a data processing system, by defining sensitivity designations for data objects stored in the system, and creating datasets by grouping metadata for data objects that are intended to classified with a same sensitivity designation, wherein each dataset spans multiple storage devices of different storage types, and wherein each dataset defines a single data sensitivity unit for the data objects referenced by a respective dataset. A sensitivity classifier component tags each dataset with a sensitivity tag to specify a protection or control operation on the data objects referenced by the respective dataset, and a processing component then processes data objects for each tagged dataset in accordance with the specified protection or control operation.

Data sensitivity classification using content-based datasets

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

Providing content-based encryption to content data in a data processing system by creating datasets by grouping metadata for data objects that are intended to be encrypted with a common encryption key, where each dataset spans multiple storage devices of different storage types, and defines a single data encryption unit for the data objects referenced by a respective dataset. Each dataset is tagged with an encryption tag to enable or disable use of a self-selected encryption key. Encryption keys stored in or made available to the system are accessed for encrypting the data objects using an encryption process. A key management component maps each dataset to a corresponding encryption key of the encryption keys, and an encryption component encrypts, for each dataset, referenced data objects using a corresponding mapped encryption key.

Encryption key management using content-based datasets

Providing content based data protection for data stored in a large-scale data storage system by scanning data stored in one or more databases for discovery of metadata, and extracting the discovered metadata, for storage in a data catalog, the data catalog having a scanning function performing the scanning step, and comprising a database storing the metadata in one or more tables. A protection policy is defined to commonly protect content data referenced by metadata in the data catalog, and applied to the referenced content data to perform a data protection operation the content data. Datasets stored in the catalog are generated by running queries on the catalog, where a query comprises metadata selectors as tags applied to the catalog, where the tags define at least one of a file type, name, location, creation time, or file characteristic.

Data catalog for dataset lifecycle management system for content-based data protection

Managing a lifecycle of data by identifying data objects that are subject to same control rules in each stage of the lifecycle as grouped data, where the control rules allow only authorized access to or authorized operations on the grouped data based on a current stage of the lifecycle. A dataset is generated for the grouped data by identifying metadata of the grouped data to be processed similarly within the lifecycle, and storing the metadata in the dataset. The control rules associated with the grouped data as stage tags for the dataset. Actions performed on the data referenced by the dataset are monitored to ensure that the monitored actions comply with control rules using the stage tags of the dataset.

Dataset lifecycle management using monitoring and ACL control for content-based datasets

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

Embodiments of monitoring data assets in a system to apply rules to optimize storage and access of the data assets based on data content rather than data location, by defining rules based on the monitoring attributes, wherein a rule dictates a storage location of selected data or access permissions to the data by one or more persons or groups in the system. The selected data is tagged with a defined metadata tag, and a dataset is created by running a query against a data catalog to derive the dataset. A component monitors data usage and access of data elements referenced by the dataset to detect any violations of the defined rules, and provides a notification of any violation to facilitate remedial action by a user or process. The dataset can span multiple storage devices of different types to define a single processing unit for the monitoring attributes.

Monitoring data usage to optimize storage placement and access using content-based datasets

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

Optimizing data movement from a source data center to a target data center by grouping metadata of data objects into a dataset that encompasses data processed identically by a data processing operation, where the dataset defines a single data access unit for these data objects, and the data processing operation processes them as a single unit based on data content rather than physical or logical data location of the data objects. A mobility process correlates an increase in a number of metadata elements in the dataset with a growth rate of the dataset, and compares the dataset growth rate with historical or similar dataset growth rate data. The comparison is used to determine when and where to move data from the source to the target data center based on a forecast of accelerated growth indicated by the comparing step, and a consideration of target data center resources, data move costs, and streaming data effects.

Optimizing cross-data center mobility using content-based datasets

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

Providing content based data protection for data stored in a large-scale data storage system by creating a dataset by grouping metadata for unstructured data objects that are grouped together by one or more filters. The dataset can span multiple storage devices of different types, so that it defines a single data protection unit for the corresponding content data. A user initiated query input through a search engine interface generates the one or more filters, and a protection policy is defined that protects the dataset as the single unit based on data content rather than data location. Datasets are stored in a catalog, and are generated by running queries on the catalog, where a query comprises metadata selectors as tags applied to the catalog, where the tags define at least one of a file type, name, location, creation time, or file characteristic.

Data query processing system for content-based data protection and dataset lifecycle management

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.

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Mission Viejo, CA, United States of America

Adam E Brenner