Abstract: Embodiments for a method of storing documents using a document data protection process. Documents are first compressed and stored in a container along with selected metadata. An Document Record is created for each document. A Container Record is created for each newly created container, and a Backup Record is created for each container for each backup. Once the required records are created, the process facilitates the execution of backup operations, such as full or incremental backups of the stored documents. Data tiering is supported so that low cost object storage in the public cloud is used instead of expensive processing methods like deduplication. A user interface receives a user setting dictating a storage media storing the container based on a relative availability of the storage media versus cost of storage.

Abstract: Optimizing backups for a data protection system by determining a size of a dataset to be saved in each backup operation and an available bandwidth in each transfer window of transfer windows for data transfers between a source and destination. An order of the backup operations is defined based on first ordering the backups based on decreasing dataset size and second ordering the transfer windows in order of decreasing bandwidth, and matching the backups to the transfer windows in accordance with these orderings. A dynamic window sizing process determines an initial change between the minimum and maximum bandwidth utilization over a period of time and then iteratively split and consolidate the time blocks until optimal utilization over the time period is reached.

Abstract: 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.

Abstract: 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.

Abstract: Optimizing multiple backup operations for multiple sources by discovering shared data routes between sources and targets for backup operations performed in a certain time period. Backups using shared routes use a Best Time Algorithm that determines a size of a dataset to be saved an available bandwidth in each transfer window of the time period, and then determines an order of the backup operations based on first ordering the backups based on decreasing dataset size and second ordering the transfer windows in order of decreasing bandwidth, and matching the backups to the transfer windows in accordance with the first ordering and second ordering. The optimum time represents a time to initiate each backup of the multiple backups that results in a shortest data transfer time over the entire time period.

Abstract: Optimizing backups for a data protection system by determining a size of a dataset to be saved in each backup operation and an available bandwidth in each transfer window of transfer windows for data transfers between a source and destination. An order of the backup operations is defined based on first ordering the backups based on decreasing dataset size and second ordering the transfer windows in order of decreasing bandwidth, and matching the backups to the transfer windows in accordance with these orderings. A dynamic window sizing process determines an initial change between the minimum and maximum bandwidth utilization over a period of time and then iteratively split and consolidate the time blocks until optimal utilization over the time period is reached.

Abstract: Embodiments for optimizing a backup operation for a data protection system, by connecting with network devices in a network implementing the backup operation, the backup operation implementing a policy that specifies a plurality of service requirements and that is initiated at a time within a plurality of time windows, and determining a size of dataset to be backed up by the backup operation. The method further monitors a flow of data between a source and a destination of data in the network, compiles historical data of operational parameters for data transfers between the source and destination, and determines an optimum time within the plurality of time windows based on the dataset size and the historical data.

Abstract: Restoring data stored by a backup process in an object store of a cloud network by initiating a restore request to a server accessing the object store, by providing a source identifier of one or more data blocks of the requested data and a timestamp for a backup image at a requested point in time (PIT). The server manages a queue of blocks in temporary storage to provide blocks for retrieval by the client, and initially fills the queue with blocks from a full backup and unchanged by any incremental backup to the requested PIT. In response to the request, the server assembles blocks that are changed from the full backup through one or more incremental backups to the requested PIT using a full block synthesis process employing forward and reverse delta objects, and sends to the client the blocks initially filled in the queue with the assembled blocks to return a full PIT backup image for the requested PIT.

Abstract: Embodiments for a storage classifier that provides recommendations to a backup server for storage targets among a plurality of disparate target storage types. The storage classifier receives metadata (name, type, size), and the Service Level Agreement with information such as: retention time, Recovery Point Objective, and Recovery Time Objective) from the backup software. The backup software itself receives policy recommendations from a data label rules engine based on certain file attributes. The storage classifier receives an initial recommendation for the storage type and location (e.g., on-premises deduplication storage or public-cloud object storage, etc.) from a data classifier. Based on these inputs, the storage classifier provides recommended specific storage targets to the backup software on a file-by-file basis for data stored in a backup operation.

Abstract: 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.

Abstract: Embodiments for optimizing multiple backup operations for a data protection system, by determining a size of a dataset to be saved in each backup operation and an available bandwidth in each transfer window of a plurality of transfer windows for transfer data between a source and destination; then determining an order of the backup operations based on first ordering the backups based on decreasing dataset size and second ordering the transfer windows in order of decreasing bandwidth, and matching the backups to the transfer windows in accordance with the first ordering and second ordering. The optimum time represents a time to initiate the backup operation that results in a shortest data transfer time over all of the transfer windows from as compared to other possible transfer windows in a defined backup period.

Abstract: 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.

Abstract: 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, 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.

Abstract: Described is a system for a Policy Derivation Engine (PDE) that, when accessed by one or more organizations, assists such organizations in updating and managing data protection policies based on external policy updates from one or more other organizations that share the same organizational peer group. The system receives a policy request from a first computing system associated with a first organization. The system computes a list of peer organizations of the first organization. The system builds policy update data based on respective policy data received from one or more peer organizations and transmits at least a portion of policy update data to the first computing system associated with the first organization.

Abstract: Reducing backup data by providing a data protection target for storing content data from clients running operating system and applications, and a common data protection target (CDPT) separate from the data protection target for storing Gold image data for the operating system and applications. Gold image data is stored by a user in a defined location or tagged with a defined tag. The stored or tagged Gold image data is identified by the backup software and copied to the CDPT. The identified Gold image data is cataloged. During backup, the CDPT is queried to determine if the Gold image exists, and if so, the DP target does not store the Gold image data in the DP target, but uses the reference to indicate the location of the Gold image data corresponding to the backed up content data. During a restore, the restore stream is built from the DP target and CDPT to combine user content data and Gold image data.

Abstract: Embodiments for recording application program interface (API) calls through an API recorder component or process that captures API calls as they are used by a number of users. API flows can be recorded to define recorded macros. An API collector collects API data for executed APIs and macros for storage in an API collection database and API performance analysis using at least one of a sequence analyzer, a runtime analyzer, a usage frequency analyzer, and an error analyzer. Output from the analyzers is provided to a user for review and determination of API improvement with respect to error reduction, API sequencing, runtime reduction, and usage frequency.

Abstract: A three-phase full quorum commit method enabling backing up of network devices that do not offer direct hooks in order to have application consistent protection. Devices are verified to be ready to perform a backup, and a condition of reaching and maintaining a full quorum of devices within a maximum time period is required before the system can be backed up. The three phase backup process reduces the chance of changes to network devices from corrupting consistency among the saved states of the different and disparate network devices. Multiple devices of different makes and models participate together as a unified backup as a network partition and all devices are verified as being in a ready state. The device configuration data is moved from device memory to local disk, and can then be tiered to secondary storage.

Abstract: Embodiments for executing selected remediation scripts to remedy issues in a data storage system through an API/script recorder component or process that presents a DVR (digital video recorder)-like interface and capability to capture scripts and API calls as they are used when navigating a graphical user interface (GUI). Internal components of this function perform the recording and macro creation to allow constant playback of these recorded and selected scripts. By easily generating and managing scripts macros, system administrators can easily re-execute customer workflows to apply known fixes to current issues in the data storage system.

Abstract: Embodiments for optimizing a cost of storing backup data in an object store of a public cloud network provided by a cloud provider by storing delta objects each embodying changes to blocks of the backup data between a full backup incremental backups. The delta objects are each of a same block size, which can be changed. The process selects two sequential delta objects to form a larger object by extending each block to a minimal part size that is larger than the block size and inserting a region of unchanged data between the two delta objects. The larger object is divided into three different parts comprising the inserted region, and each of the extended delta object blocks. The process calculates an API and a compute cost for a synthesis operation on the delta objects based on a cost model and current price schedule of the cloud provider for the three different parts, and for the substantially larger object as a whole. The process selects a block size for the delta objects based on a lowest calculated cost.

Abstract: Storing documents in an object store in the cloud by creating a container for the documents, defining a document record including unique document ID and an index. A backup record for every backup includes a timestamp for each backup, and a bitmask for each document in the container has a single bit position for each document to indicate the corresponding document as unchanged or that it has been changed/deleted. Each bit location in the bitmask is referenced by the index in the document record. Large documents have a multi-part record identifying the containers. During point-in-time recovery, containers that match a given timestamp are found, and documents where the bitmask bit is set are restored.