Abstract: A backup system is described for prioritizing backup data in enterprise networks. Messages containing data to be backed up are received at a backup server from endpoint devices and a priority value is determined for each message based on numerous factors, such as the organizational role of the user of the endpoint, the time since last backup, average upload speed, frequency of backups, and other properties. The system prioritizes backing up of messages based on the priority value of the messages.

Abstract: Distributing an update from a server to multiple endpoint devices that reside on the same local network. The server provides information to each client device identifying all other devices located on the same local network that will also need to receive the update. Each client device sorts the files and begins the process of downloading the files by checking, for each file in sequence, whether another device on the same network has downloaded the file or is currently in the process of downloading. If another device on the same network has completed downloading the file, the device requests the file from the other client device. If another device is currently in the process of downloading, the device places the file at the end of the sequence. If the file has not been downloaded and is not being downloaded, the endpoint device initiates downloading the file from the server.

Abstract: Local distributed backup techniques are described, where each client device calculates a manifest of its files and sends the manifest to a central server. The server performs deduplication based on the received manifest to determine which files are missing and need to be backed up, and notifies the client of those files. At this point, instead of uploading the missing files to the central server, the files are instead uploaded to another peer client device that has sufficient disk space and resides in the network vicinity of the original client device. The upload may be done to multiple peers for redundancy. Upon backup completion, the client state may be left as-is, in which case the client periodically uploads incremental updates to keep any changes synchronized with the backup peer. Alternatively, the client can be centralized to the central server, but at a later time when more resources are available.

Abstract: Techniques are described for reducing I/O operations and storage capacity requirements for centralized backup storage systems. A central server optimizes the collection and centralization of backup data from a number of endpoint devices for backup purposes. The central server utilizes a single instance store and a persistent files cache to minimize the number of backup copies for each non-unique file, reduce storage usage, network traffic, memory footprint and CPU cycles required to identify and process non-unique data. For each file in the single instance store, the server tracks the source device of that file until a threshold number of devices have been reached. Once the file reaches the threshold number of sources, the file is marked as persistent and its hash value is placed in the persistent files cache. Thereafter, all pointer creation and reference counting for that file cease.

Abstract: Local distributed backup techniques are described, where each client device calculates a manifest of its files and sends the manifest to a central server. The server performs deduplication based on the received manifest to determine which files are missing and need to be backed up, and notifies the client of those files. At this point, instead of uploading the missing files to the central server, the files are instead uploaded to another peer client device that has sufficient disk space and resides in the network vicinity of the original client device. The upload may be done to multiple peers for redundancy. Upon backup completion, the client state may be left as-is, in which case the client periodically uploads incremental updates to keep any changes synchronized with the backup peer. Alternatively, the client can be centralized to the central server, but at a later time when more resources are available.

Abstract: Techniques are described for reducing I/O operations and storage capacity requirements for centralized backup storage systems. A central server optimizes the collection and centralization of backup data from a number of endpoint devices for backup purposes. The central server utilizes a single instance store and a persistent files cache to minimize the number of backup copies for each non-unique file, reduce storage usage, network traffic, memory footprint and CPU cycles required to identify and process non-unique data. For each file in the single instance store, the server tracks the source device of that file until a threshold number of devices have been reached. Once the file reaches the threshold number of sources, the file is marked as persistent and its hash value is placed in the persistent files cache. Thereafter, all pointer creation and reference counting for that file cease.

Abstract: Techniques are described for reducing I/O operations and storage capacity requirements for centralized backup storage systems. A central server optimizes the collection and centralization of backup data from a number of endpoint devices for backup purposes. The central server utilizes a single instance store and a persistent files cache to minimize the number of backup copies for each non-unique file, reduce storage usage, network traffic, memory footprint and CPU cycles required to identify and process non-unique data. For each file in the single instance store, the server tracks the source device of that file until a threshold number of devices have been reached. Once the file reaches the threshold number of sources, the file is marked as persistent and its hash value is placed in the persistent files cache. Thereafter, all pointer creation and reference counting for that file cease.

Abstract: A backup system is described for prioritizing backup data in enterprise networks. Messages containing data to be backed up are received at a backup server from endpoint devices and a priority value is determined for each message based on numerous factors, such as the organizational role of the user of the endpoint, the time since last backup, average upload speed, frequency of backups, and other properties. The system prioritizes backing up of messages based on the priority value of the messages.

Abstract: Distributing an update from a server to multiple endpoint devices that reside on the same local network. The server provides information to each client device identifying all other devices located on the same local network that will also need to receive the update. Each client device sorts the files and begins the process of downloading the files by checking, for each file in sequence, whether another device on the same network has downloaded the file or is currently in the process of downloading. If another device on the same network has completed downloading the file, the device requests the file from the other client device. If another device is currently in the process of downloading, the device places the file at the end of the sequence. If the file has not been downloaded and is not being downloaded, the endpoint device initiates downloading the file from the server.

Abstract: Techniques are described for reducing I/O operations and storage capacity requirements for centralized backup storage systems. A central server optimizes the collection and centralization of backup data from a number of endpoint devices for backup purposes. The central server utilizes a single instance store and a persistent files cache to minimize the number of backup copies for each non-unique file, reduce storage usage, network traffic, memory footprint and CPU cycles required to identify and process non-unique data. For each file in the single instance store, the server tracks the source device of that file until a threshold number of devices have been reached. Once the file reaches the threshold number of sources, the file is marked as persistent and its hash value is placed in the persistent files cache. Thereafter, all pointer creation and reference counting for that file cease.