Abstract: With a forever incremental snapshot configuration and a typical caching policy (e.g., least recently used), a storage appliance may evict stable data blocks of an older snapshot, perhaps unchanged data blocks of the snapshot baseline. If stable data blocks have been evicted, restore of a recent snapshot will suffer the time penalty of downloading the stable blocks for restoring the recent snapshot. Creating synthetic baseline snapshots and refreshing eviction data of stable data blocks can avoid eviction of stable data blocks and reduce the risk of violating a recovery time objective.

Abstract: A storage tier manager efficiently creates different representations of a dataset backup for different retention periods. Each of the representations of the dataset backup is distinctly identifiable despite initially representing a same dataset backup. The representations are structured metadata corresponding to the dataset backup. One representation is a cached backup version of the dataset backup (“cached backup” or “cached representation”) provided for low latency access while residing at a storage tier of the backup appliance for a relatively short retention period according to a lifecycle management policy. The other representation is a cloud backup version of the dataset backup (“cloud backup” or “cloud representation”) provided for persisting into cloud storage for a longer retention period according to the lifecycle management policy.

Abstract: With a forever incremental snapshot configuration and a typical caching policy (e.g., least recently used), a storage appliance may evict stable data blocks of an older snapshot, perhaps unchanged data blocks of the snapshot baseline. If stable data blocks have been evicted, restore of a recent snapshot will suffer the time penalty of downloading the stable blocks for restoring the recent snapshot. Creating synthetic baseline snapshots and refreshing eviction data of stable data blocks can avoid eviction of stable data blocks and reduce the risk of violating a recovery time objective.

Abstract: With a forever incremental snapshot configuration and a typical caching policy (e.g., least recently used), a storage appliance may evict stable data blocks of an older snapshot, perhaps unchanged data blocks of the snapshot baseline. If stable data blocks have been evicted, restore of a recent snapshot will suffer the time penalty of downloading the stable blocks for restoring the recent snapshot. Creating synthetic baseline snapshots and refreshing eviction data of stable data blocks can avoid eviction of stable data blocks and reduce the risk of violating a recovery time objective.

Abstract: A storage appliance can be designed to facilitate efficient restore of multiple backed up files in a system that allows files to share data blocks. A data management application or storage OS names data blocks and communicates those names to the storage appliance when backing up to or through the storage appliance. The storage appliance can leverage the data block names when restoring a group of files by restoring at data block granularity instead of file granularity. Restoring at the granularity of the data blocks by their names allows the storage appliance to avoid repeatedly sending a same data block to the restore requestor (e.g., a storage OS or data management application) while still instructing the restore requestor how to reconstruct the corresponding file(s) with mappings between valid data ranges and the named data blocks.

Abstract: A storage tier manager creates different versions of a dataset backup for different retention periods. Each of the versions is distinctly identifiable despite initially representing a same dataset backup. One version can be referred to as a cached version of the dataset backup and another version can be referred to as a cloud version of the dataset backup. When the retention period expires for the cached version of the dataset backup, the storage tier manager migrates the cloud version of the dataset backup from the caching storage tier to the cloud storage tier. The storage tier manager can then recover storage space occupied by data that has been migrated, as long as that data is not shared with other cached versions of other dataset backups due to deduplication.

Abstract: A storage tier manager creates different representations of a dataset backup for different retention periods. Each of the representations of the dataset backup is distinctly identifiable despite initially representing a same dataset backup. The representations are structured metadata corresponding to the dataset backup. One representation is a cached backup version of the dataset backup (“cached backup” or “cached representation”) provided for low latency access while residing at a storage tier of the backup appliance for a relatively short retention period according to a lifecycle management policy. The other representation is a cloud backup version of the dataset backup (“cloud backup” or “cloud representation”) provided for persisting into cloud storage for a longer retention period according to the lifecycle management policy.

Abstract: With a forever incremental snapshot configuration and a typical caching policy (e.g., least recently used), a storage appliance may evict stable data blocks of an older snapshot, perhaps unchanged data blocks of the snapshot baseline. If stable data blocks have been evicted, restore of a recent snapshot will suffer the time penalty of downloading the stable blocks for restoring the recent snapshot. Creating synthetic baseline snapshots and refreshing eviction data of stable data blocks can avoid eviction of stable data blocks and reduce the risk of violating a recovery time objective.

Abstract: With a forever incremental snapshot configuration and a typical caching policy (e.g., least recently used), a storage appliance may evict stable data blocks of an older snapshot, perhaps unchanged data blocks of the snapshot baseline. If stable data blocks have been evicted, restore of a recent snapshot will suffer the time penalty of downloading the stable blocks for restoring the recent snapshot. Creating synthetic baseline snapshots and refreshing eviction data of stable data blocks can avoid eviction of stable data blocks and reduce the risk of violating a recovery time objective.

Abstract: Intelligent snapshot tiering facilitates efficient management of snapshots and efficient restore of snapshots. For intelligent snapshot tiering, a storage appliance can limit cross-tier migration to invalidated data blocks of a snapshot instead of an entire snapshot. Based on a policy, a storage appliance can identify a snapshot to be migrated to another storage tier and then determine which data blocks are invalidated by an immediately succeeding snapshot. This would limit network bandwidth consumption to the invalidated data blocks and maintain the valid data blocks at the faster access storage tier since the more recent snapshots are more likely to be restored.

Abstract: Intelligent snapshot tiering facilitates efficient management of snapshots and efficient restore of snapshots. For intelligent snapshot tiering, a storage appliance can limit cross-tier migration to invalidated data blocks of a snapshot instead of an entire snapshot. Based on a policy, a storage appliance can identify a snapshot to be migrated to another storage tier and then determine which data blocks are invalidated by an immediately succeeding snapshot. This would limit network bandwidth consumption to the invalidated data blocks and maintain the valid data blocks at the faster access storage tier since the more recent snapshots are more likely to be restored.

Abstract: A storage appliance can be designed to facilitate efficient restore of multiple backed up files in a system that allows files to share data blocks. A data management application or storage OS names data blocks and communicates those names to the storage appliance when backing up to or through the storage appliance. The storage appliance can leverage the data block names when restoring a group of files by restoring at data block granularity instead of file granularity. Restoring at the granularity of the data blocks by their names allows the storage appliance to avoid repeatedly sending a same data block to the restore requestor (e.g., a storage OS or data management application) while still instructing the restore requestor how to reconstruct the corresponding file(s) with mappings between valid data ranges and the named data blocks.

Abstract: With a forever incremental snapshot configuration and a typical caching policy (e.g., least recently used), a storage appliance may evict stable data blocks of an older snapshot, perhaps unchanged data blocks of the snapshot baseline. If stable data blocks have been evicted, restore of a recent snapshot will suffer the time penalty of downloading the stable blocks for restoring the recent snapshot. Creating synthetic baseline snapshots and refreshing eviction data of stable data blocks can avoid eviction of stable data blocks and reduce the risk of violating a recovery time objective.

Abstract: Metadata generate for incremental backup is disclosed. A subset of blocks used to store file system metadata are identified in a set of blocks changed since a last backup. File system metadata stored in the subset of blocks is used to obtain file system metadata associated with file system objects that have been created and/or modified since the last backup. The file system metadata associated with file system objects that have been created and/or modified since the last backup is used to generate file system metadata files for the incremental backup.

Abstract: A storage tier manager creates different representations of a dataset backup for different retention periods. Each of the representations of the dataset backup is distinctly identifiable despite initially representing a same dataset backup. The representations are structured metadata corresponding to the dataset backup. One representation is a cached backup version of the dataset backup (“cached backup” or “cached representation”) provided for low latency access while residing at a storage tier of the backup appliance for a relatively short retention period according to a lifecycle management policy. The other representation is a cloud backup version of the dataset backup (“cloud backup” or “cloud representation”) provided for persisting into cloud storage for a longer retention period according to the lifecycle management policy.

Abstract: A storage tier manager efficiently creates different representations of a dataset backup for different retention periods. Each of the representations of the dataset backup is distinctly identifiable despite initially representing a same dataset backup. The representations are structured metadata corresponding to the dataset backup. One representation is a cached backup version of the dataset backup (“cached backup” or “cached representation”) provided for low latency access while residing at a storage tier of the backup appliance for a relatively short retention period according to a lifecycle management policy. The other representation is a cloud backup version of the dataset backup (“cloud backup” or “cloud representation”) provided for persisting into cloud storage for a longer retention period according to the lifecycle management policy.

Abstract: A storage tier manager creates different versions of a dataset backup for different retention periods. Each of the versions is distinctly identifiable despite initially representing a same dataset backup. One version can be referred to as a cached version of the dataset backup and another version can be referred to as a cloud version of the dataset backup. When the retention period expires for the cached version of the dataset backup, the storage tier manager migrates the cloud version of the dataset backup from the caching storage tier to the cloud storage tier. The storage tier manager can then recover storage space occupied by data that has been migrated, as long as that data is not shared with other cached versions of other dataset backups due to deduplication.

Abstract: A method for information management comprises intercepting an output from an application; distributing packets according to a routing scheme, wherein the packets are associated with the output, and wherein distributing the packets may occur when the application is associated with a first operating system, and may also occur when the application is associated with a second operating system; and storing the packets.

Abstract: Metadata generate for incremental backup is disclosed. A subset of blocks used to store file system metadata are identified in a set of blocks changed since a last backup. File system metadata stored in the subset of blocks is used to obtain file system metadata associated with file system objects that have been created and/or modified since the last backup. The file system metadata associated with file system objects that have been created and/or modified since the last backup is used to generate file system metadata files for the incremental backup.

Abstract: Techniques are disclosed for quiescing a file system. One technique comprises freezing a file system, flushing a memory associated with the file system, and thawing the file system. One technique comprises receiving a request targeted to a file system and intercepting the request.