Abstract: Systems and methods for storage pruning can enable users to delete, edit, or copy backed up data that matches a pattern. Storage pruning can enable fine-grain deletion or copying of these files from backups stored in secondary storage devices. Systems and methods can also enable editing of metadata associated with backups so that when the backups are restored or browsed, the logical edits to the metadata can then be performed physically on the data to create a custom restore or a custom view. A user may perform operations such as renaming, deleting, modifying flags, and modifying retention policies on backed up items. Although the underlying data in the backup may not change, the view of the backup data when the user browses the backup data can appear to include the user's changes. A restore of the data can cause those changes to be performed on the backup data.

Abstract: A method and system described herein for classifying data of virtual machines in a heterogeneous computing comprising virtual machines and non-virtual machines. The system may access a secondary copy of data stored by a virtual machine, create metadata associated with that data, store the metadata in an index that comprises metadata associated with data stored on non-virtual machines, using a journal file, determine modified data objects within the data stored by the virtual machine, access or create metadata associated with modified data objects, and update the index accordingly.

Abstract: Certain embodiments disclosed herein reduce or eliminate a communication bottleneck at the storage manager by reducing communication with the storage manager while maintaining functionality of an information management system. In some implementations, a client obtains information for enabling a secondary storage job (e.g., a backup or restore) from a storage manager and stores the information (which may be referred to as job metadata) in a local cache. The client may then reuse the job metadata for multiple storage jobs reducing the frequency of communication with the storage manager. When a configuration of the information management system changes, or the availability of resources changes, the storage manager can push updates to the job metadata to the clients. Further, a client can periodically request updated job metadata from the storage manager ensuring that the client does not rely on out-of-date job metadata.

Abstract: This application relates to targeted search of backup data. A data storage system can provide a targeted search of backup data using facial recognition. Upon receiving a search query that identifies a specific user whose profile is stored in a user database, the data storage system can retrieve a profile photograph of the specific user, perform facial recognition on photographs included in the backup data, and output a search result including a set of photographs in the backup data that match the facial features of the specific user.

Abstract: An information management system is provided herein that uses machine learning (ML) to predict what data to store in a secondary storage device and/or when to perform the storage. For example, a client computing device can be initially configured to store data in a secondary storage device according to one or more storage policies. A media agent in the information management system can monitor data usage on the client computing device, using the data usage data to train a data storage ML model. The data storage ML model may be trained such that the model predicts what data to store in a secondary storage device and/or when to perform the storage. The client computing device can then be configured to use the trained data storage ML model in place of the storage polic(ies) to determine which data to store in a secondary storage device and/or when to perform the storage.

Abstract: Described in detail herein is a method of copying data of one or more virtual machines being hosted by one or more non-virtual machines. The method includes receiving an indication that specifies how to perform a copy of data of one or more virtual machines hosted by one or more virtual machine hosts. The method may include determining whether the one or more virtual machines are managed by a virtual machine manager that manages or facilitates management of the virtual machines. If so, the virtual machine manager is dynamically queried to automatically determine the virtual machines that it manages or that it facilitates management of. If not, a virtual machine host is dynamically queried to automatically determine the virtual machines that it hosts. The data of each virtual machine is then copied according to the specifications of the received indication.

Abstract: Certain embodiments described herein relate to an improved disk usage growth prediction system. In some embodiments, one or more components in an information management system can determine usage status data of a given storage device, perform a validation check on the usage status data using multiple prediction models, compare validation results of the multiple prediction models to identify the best performing prediction model, generate a disk usage growth prediction using the identified prediction model, and adjust the available space of the storage device according to the disk usage growth prediction.

Abstract: Certain embodiments disclosed herein reduce or eliminate a communication bottleneck at the storage manager by reducing communication with the storage manager while maintaining functionality of an information management system. In some implementations, a client obtains information for enabling a secondary storage job (e.g., a backup or restore) from a storage manager and stores the information (which may be referred to as job metadata) in a local cache. The client may then reuse the job metadata for multiple storage jobs reducing the frequency of communication with the storage manager. When a configuration of the information management system changes, or the availability of resources changes, the storage manager can push updates to the job metadata to the clients. Further, a client can periodically request updated job metadata from the storage manager ensuring that the client does not rely on out-of-date job metadata.

Abstract: Indexing preferences generally associate each data source with a type of indexing technology and/or with an index/catalog and/or with a computing device that hosts the index/catalog for tracking backup data generated from the source data. Indexing preferences govern which index/catalog receives transaction logs for a given storage operation. Thus, indexing destinations are defined granularly and flexibly in reference to the source data. Load balancing without user intervention assures that the various index/catalogs are fairly distributed in the illustrative backup systems by autonomously initiating migration jobs. Criteria for initiating migration jobs are based on past usage and going-forward trends. An illustrative migration job re-associates data sources with a different destination media agent and/or index/catalog, including transferring some or all relevant transaction logs and/or indexing information from the old host to the new host.

Abstract: This application relates to targeted search of backup data. A data storage system can provide a targeted search of backup data using facial recognition. Upon receiving a search query that identifies a specific user whose profile is stored in a user database, the data storage system can retrieve a profile photograph of the specific user, perform facial recognition on photographs included in the backup data, and output a search result including a set of photographs in the backup data that match the facial features of the specific user.

Abstract: Certain embodiments disclosed herein reduce or eliminate a communication bottleneck at the storage manager by reducing communication with the storage manager while maintaining functionality of an information management system. In some implementations, a client obtains information for enabling a secondary storage job (e.g., a backup or restore) from a storage manager and stores the information (which may be referred to as job metadata) in a local cache. The client may then reuse the job metadata for multiple storage jobs reducing the frequency of communication with the storage manager. When a configuration of the information management system changes, or the availability of resources changes, the storage manager can push updates to the job metadata to the clients. Further, a client can periodically request updated job metadata from the storage manager ensuring that the client does not rely on out-of-date job metadata.

Abstract: According to certain aspects, a system can include a client computing device configured to: in response to user interaction, store an identifier associated with a first tag in association with a first file; and in response to instructions to perform a secondary copy operation, forward the first file, a second file, and the identifier associated with the first tag. The system may also include a secondary storage controller computer(s) configured to: based on a review of the identifier associated with the first tag, identify the first file as having been tagged with the first tag; electronically obtain rules associated with the first tag; perform on the first file at least a first secondary storage operation specified by the rules associated with the first tag; and perform on the second file at least a second secondary storage operation, wherein the first and second secondary storage operations are different.

Abstract: An information management system is provided herein that uses machine learning (ML) to predict what data to store in a secondary storage device and/or when to perform the storage. For example, a client computing device can be initially configured to store data in a secondary storage device according to one or more storage policies. A media agent in the information management system can monitor data usage on the client computing device, using the data usage data to train a data storage ML model. The data storage ML model may be trained such that the model predicts what data to store in a secondary storage device and/or when to perform the storage. The client computing device can then be configured to use the trained data storage ML model in place of the storage polic(ies) to determine which data to store in a secondary storage device and/or when to perform the storage.

Abstract: The data storage system according to certain aspects can filter secondary copies of data (e.g., backups, snapshots, archives, etc.) generated by multiple client computing devices into a single, filtered, global reference copy. A reference copy may be a filtered view or representation of secondary storage data in a data storage system. A reference copy may include a data structure that includes references to a subset of secondary storage data that meets certain filtering criteria. The filtering criteria may be specified by users according to user preference. Data included in a reference copy may be stored in native format (e.g., format of the application that generated the data) and be accessible through the application associated with the data.

Abstract: Certain embodiments disclosed herein reduce or eliminate a communication bottleneck at the storage manager by reducing communication with the storage manager while maintaining functionality of an information management system. In some implementations, a client obtains information for enabling a secondary storage job (e.g., a backup or restore) from a storage manager and stores the information (which may be referred to as job metadata) in a local cache. The client may then reuse the job metadata for multiple storage jobs reducing the frequency of communication with the storage manager. When a configuration of the information management system changes, or the availability of resources changes, the storage manager can push updates to the job metadata to the clients. Further, a client can periodically request updated job metadata from the storage manager ensuring that the client does not rely on out-of-date job metadata.

Abstract: Certain embodiments disclosed herein reduce or eliminate a communication bottleneck at the storage manager by reducing communication with the storage manager while maintaining functionality of an information management system. In some implementations, a client obtains information for enabling a secondary storage job (e.g., a backup or restore) from a storage manager and stores the information (which may be referred to as job metadata) in a local cache. The client may then reuse the job metadata for multiple storage jobs reducing the frequency of communication with the storage manager. When a configuration of the information management system changes, or the availability of resources changes, the storage manager can push updates to the job metadata to the clients. Further, a client can periodically request updated job metadata from the storage manager ensuring that the client does not rely on out-of-date job metadata.

Abstract: Certain embodiments disclosed herein reduce or eliminate a communication bottleneck at the storage manager by reducing communication with the storage manager while maintaining functionality of an information management system. In some implementations, a client obtains information for enabling a secondary storage job (e.g., a backup or restore) from a storage manager and stores the information (which may be referred to as job metadata) in a local cache. The client may then reuse the job metadata for multiple storage jobs reducing the frequency of communication with the storage manager. When a configuration of the information management system changes, or the availability of resources changes, the storage manager can push updates to the job metadata to the clients. Further, a client can periodically request updated job metadata from the storage manager ensuring that the client does not rely on out-of-date job metadata.

Abstract: Certain embodiments disclosed herein reduce or eliminate a communication bottleneck at the storage manager by reducing communication with the storage manager while maintaining functionality of an information management system. In some implementations, a client obtains information for enabling a secondary storage job (e.g., a backup or restore) from a storage manager and stores the information (which may be referred to as job metadata) in a local cache. The client may then reuse the job metadata for multiple storage jobs reducing the frequency of communication with the storage manager. When a configuration of the information management system changes, or the availability of resources changes, the storage manager can push updates to the job metadata to the clients. Further, a client can periodically request updated job metadata from the storage manager ensuring that the client does not rely on out-of-date job metadata.

Abstract: The data storage system according to certain aspects can filter secondary copies of data (e.g., backups, snapshots, archives, etc.) generated by multiple client computing devices into a single, filtered, global reference copy. A reference copy may be a filtered view or representation of secondary storage data in a data storage system. A reference copy may include a data structure that includes references to a subset of secondary storage data that meets certain filtering criteria. The filtering criteria may be specified by users according to user preference. Data included in a reference copy may be stored in native format (e.g., format of the application that generated the data) and be accessible through the application associated with the data.

Abstract: An information management system is provided herein that uses machine learning (ML) to predict what data to store in a secondary storage device and/or when to perform the storage. For example, a client computing device can be initially configured to store data in a secondary storage device according to one or more storage policies. A media agent in the information management system can monitor data usage on the client computing device, using the data usage data to train a data storage ML model. The data storage ML model may be trained such that the model predicts what data to store in a secondary storage device and/or when to perform the storage. The client computing device can then be configured to use the trained data storage ML model in place of the storage polic(ies) to determine which data to store in a secondary storage device and/or when to perform the storage.