Abstract: Techniques and mechanisms are provided to support live file optimization. Active I/O access to an optimization target is monitored during optimization. Active files need not be taken offline or made unavailable to an application during optimization and retain the ability to support file operations such as read, write, unlink, and truncate while an optimization engine performs deduplication and/or compression on active file ranges.

Abstract: Techniques and mechanisms are provided to allow for selective optimization, including deduplication and/or compression, of portions of files and data blocks. Data access is monitored to generate a heat index for identifying sections of files and volumes that are frequently and infrequently accessed. These frequently used portions may be left non-optimized to reduce or eliminate optimization I/O overhead. Infrequently accessed portions can be more aggressively optimized.

Abstract: Mechanisms are provided for efficiently detecting segments for deduplication. Data is analyzed to determine file types and file components. File types such as images may have optimal data segment boundaries set at the file boundaries. Other file types such as container files are delayered to extract objects to set optimal data segment boundaries based on file type or based on the boundaries of the individual objects. Storage of unnecessary information is minimized in a deduplication dictionary while allowing for effective deduplication.

Abstract: Mechanisms are provided for performing efficient compression and deduplication of data segments. Compression algorithms are learning algorithms that perform better when data segments are large. Deduplication algorithms, however, perform better when data segments are small, as more duplicate small segments are likely to exist. As an optimizer is processing and storing data segments, the optimizer applies the same compression context to compress multiple individual deduplicated data segments as though they are one segment. By compressing deduplicated data segments together within the same context, data reduction can be improved for both deduplication and compression. Mechanisms are applied to compensate for possible performance degradation.

Abstract: Mechanisms are provided for performing network efficient deduplication. Segments are extracted from files received for deduplication at a host connected to a target over one or more networks and/or fabrics in a deduplication system. Segment identifiers (IDs) are determined and compared with segment IDs for segments already deduplicated. Segments already deduplicated need not be transmitted to a target system. References and reference counts are modified at a target system. Updating references and reference counts may involve modifying filemaps, dictionaries, and datastore suitcases for both already deduplicated and not already deduplicated segments.

Abstract: Mechanisms are provided for efficiently determining commonality in a deduplicated data set in a scalable manner regardless of the number of deduplicated files or the number of stored segments. Information is generated and maintained during deduplication to allow scalable and efficient determination of data segments shared in a particular file, other files sharing data segments included in a particular file, the number of files sharing a data segment, etc. Data need not be expanded or uncompressed. Deduplication processing can be validated and verified during commonality detection.

Abstract: A system provides file aware block level deduplication in a system having multiple clients connected to a storage subsystem over a network such as an Internet Protocol (IP) network. The system includes client components and storage subsystem components. Client components include a walker that traverses the namespace looking for files that meet the criteria for optimization, a file system daemon that rehydrates the files, and a filter driver that watches all operations going to the file system. Storage subsystem components include an optimizer resident on the nodes of the storage subsystem. The optimizer can use idle processor cycles to perform optimization. Sub-file compression can be performed at the storage subsystem.

Abstract: Mechanisms are provided for efficiently improving a dictionary used for data deduplication. Dictionaries are used to hold hash key and location pairs for deduplicated data. Strong hash keys prevent collisions but weak hash keys are more computation and storage efficient. Mechanisms are provided to use both a weak hash key and a strong hash key. Weak hash keys and corresponding location pairs are stored in an improved dictionary while strong hash keys are maintained with the deduplicated data itself. The need for having uniqueness from a strong hash function is balanced with the deduplication dictionary space savings from a weak hash function.

Abstract: A system includes a mobile unit, a client and a remote server. The mobile unit is adapted to acquire information about a region near the mobile unit, determine a location of the mobile unit and transmit an indication of the information and location. The remote server is adapted to communicate with the mobile unit to receive the indication from the mobile unit and communicate at least some of the information to the client.

Abstract: A method that is useable with a wireless medium and local stations includes communicating a request between one of the local stations and a central authority to reserve a time slot for transmitting from the local station. The central authority is used to selectively reserve the time slot based on at least in part a reservation schedule. If the central authority reserves the time slot, then during the time slot, the central authority prevents the other local stations from transmitting.