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: Embodiments for automating backup policy application to users in a data protection system of an organization, by defining a plurality of backup policies to apply to data processed by users in the organization, wherein each backup policy dictates a different performance characteristic based on storage cost and target storage type and location. Next, identifying a hierarchical position of a user within the organization and determining communication and grouping behavior of the user within the organization. A score is calculated for the user based on their hierarchical position and communication and grouping behavior, and an appropriate backup policy is assigned to a data processing device operated by the user based on the total score for the user.

Abstract: Storing data in an object store in cloud-based storage for data protection applications to address key user workflows, and provide an easily and seamlessly scalable system that minimizes the cost economics of the various resources, considers how backups are transmitted and which target data is most important during a restore operation. The system uses a client-server model in which data sources in a client store data to a cloud-based server that manages each data source as a separate namespace. The server process breaks up each data source into a series of fixed size sequential blocks, and uses a specialized change record type comprising reverse delta objects to synthesize full backup images from a first full image, wherein the full backup images are stored for every point in time in the object store. This provides a full backup to be available for client retrieval at any desired point in time. A simple block hash catalog is provided to reduce data duplication.

Abstract: Embodiments for automating backup policy application to users in a data protection system of an organization, by defining a plurality of backup policies to apply to data processed by users in the organization, wherein each backup policy dictates a different performance characteristic based on storage cost and target storage type and location. Next, identifying a hierarchical position of a user within the organization and determining communication and grouping behavior of the user within the organization. A score is calculated for the user based on their hierarchical position and communication and grouping behavior, and an appropriate backup policy is assigned to the user based on their total score.

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: 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 automating backup policies applied to users in an organization by defining backup policies based on hierarchical positions of users within the organization as modified by any communication and grouping behavior of the user within the organization. A social graph generator utilizes relevant relationships revealed by active participant communications to create a greater knowledge of data usage within the enterprise to generate social graphs that quantify a type of commonality between people. The integration of social graph information in calculating a score based on hierarchical data adds organizational awareness to the process by factoring in people's communication patterns within the organization and leverages any links that are revealed by such patterns.

Abstract: 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.

Abstract: Controlling data retention cost in cloud-based object storage by first obtaining current storage prices from the cloud provider for their different available storage tiers, and receiving user input for different possible retention parameters identity of data source and the retention period per storage tier. A cost calculator calculates the cost per time unit for each storage tier based on the current provider-specified price and the user-specified period. The calculated costs are displayed to the user a UI in tabular and/or graphical form. The user can change the retention period or data source parameters, and any impact of such changes on the cost will be calculated in real time and reflected in the graphical display. If the user changes the retention period to force an immediate movement of data from one tier to another, a data mover control facilitates this movement in the cloud object storage.

Abstract: A method and system of providing estimates or recommendations for software lifecycle management tasks by obtaining and storing local timing and result data for a current run of an application task on a local client, and gathering global timing and result data in a server computer for a plurality of clients including the local client. The local client combines its local timing and result data with the global timing and result data to generate an estimated time completion and success probability for a next run of the application task. The local client uses a rule definition file to substitute function variables for text strings and recommendation text with the appropriate values from the combined timing and result data and outputs the final time estimate UI string and recommendation through a user interface for display to a user.

Abstract: A retention-based consistent hash ring process defines each file name in the system to include its expiration date (or time) as a prefix or suffix that is stored and indexed as metadata. The process uses a virtual node to represent adjacent expiration days to create virtual nodes based on individual days of the week. Each physical node contains the same number of labeled virtual nodes, and the consistent hash ring process is used to move files with the same expiration day to different physical nodes by looking for next labeled virtual nodes on the hash ring. This provides a way to locate the virtual node storage location by specifying a file's expiration date as part of the key used in the hash ring process, and distributes files that may otherwise be assigned to the same physical node through a backup policy.

Abstract: Facilitating efficient copy reuse of point-in-time (PIT) backup data in a data storage system by providing a data protection target (DPT) for storing user the PIT backup data, and a common data protection target (CDPT) accessible to but separate from the data protection target for storing Gold image data comprising structural data for operating system and application programs as defined by a manufacturer and different from the backed user content data. A Gold image copy reuse coordinator component or process receives a selection of a Gold image to be combined with a specified PIT backup dataset, and combines the specified PIT backup dataset with the selected Gold image to form a synthetic copy of the specified PIT backup dataset stored in the DPT. The synthetic copy can then be exposed to a system through a file share protocol for reuse by a user.

Abstract: Embodiments for migrating hash values for backup data blocks in a network of data protection targets (DPTs) and a common data protection target implementing a Gold image library management system in which backups of Gold images used as templates for physical machines and virtual machines are stored on the CDPT. The CDPT and each DPT stores backup data split into chunks that are uniquely identified by a respective hash of its contents, and maintains data structures comprising the hash, chunk size, chunk data, and a list of DPT and CDPT identifiers. The hashes are partitioned into a set of buckets in the CDPT. A Bloom filter is generated for each bucket of hashes, and stored in each DPT so that each DPT stores Bloom filters for all CDPTs in the network. Each DPT checks its list of hashes against the Bloom filters in each of the DPTs to determine whether to keep or free chunks of data.

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: Automatically updating operating system and application programs in a large-scale network using Gold image data. An asset update process receives validation by a user for use of an updated program comprising new Gold image data. The process automatically updates the previous version of the updated version with the updated program to generate new user content data, without requiring further user intervention by using a defined tag linking the new Gold image data with previous Gold image data for the older version of the program. The Gold image data is stored in a central data protection target storage separate from data protection target storage for the user content data.

Abstract: 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.

Abstract: Embodiments for recording application program interface (API) calls through an API recorder component or process that presents a DVR (digital video recorder)-like interface and capability to capture 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 API steps. By easily generating and managing macros, system administrators can easily re-execute customer workflows to alleviate frustration and mistakes associated with repetitive and time-consuming input and/or coding operations.

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: 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.