Abstract: Provided are a computer program product, system, and method for determining a storage pool in which to store a data object. Event messages are received. Each event message includes metadata on an object updated in a storage pool of a plurality of storage pools, at least two of the storage pools have different reliability ratings. The event messages are added to a message queue. A determination is made of a data classification of a changed data object indicated in an event message in the message queue. The changed data object is moved to a storage pool having a reliability rating satisfying a reliability rating associated with the data classification of the changed data object.

Abstract: A computer-implemented method according to one embodiment includes determining a classification associated with an instance of data, determining a vulnerability level of a first storage system at which the instance of data is currently located, and conditionally migrating the instance of data from the first storage system to a second storage system, based on the classification associated with the instance of data and the vulnerability level of the first storage system.

Abstract: A method for scheduling data services in a heterogeneous storage environment is disclosed. In one embodiment, such a method includes instrumenting, in a heterogeneous storage environment, each of a plurality of storage systems to generate events when operations are performed thereon. The events are detected and indexed in a single namespace. These events may then be analyzed to generate a statistical model of I/O activity occurring in the heterogeneous storage environment over a period of time. From the statistical model, the method determines periods of reduced I/O workload across the heterogeneous storage environment. The method then schedules data services to occur during these periods of reduced I/O workload. A corresponding system and computer program product are also disclosed.

Abstract: A method for scheduling data services in a heterogeneous storage environment is disclosed. In one embodiment, such a method includes instrumenting, in a heterogeneous storage environment, each of a plurality of storage systems to generate events when operations are performed thereon. The events are detected and indexed in a single namespace. These events may then be analyzed to generate a statistical model of I/O activity occurring in the heterogeneous storage environment over a period of time. From the statistical model, the method determines periods of reduced I/O workload across the heterogeneous storage environment. The method then schedules data services to occur during these periods of reduced I/O workload. A corresponding system and computer program product are also disclosed.

Abstract: In one embodiment, a method includes receiving, at a centralized index operating in a cloud environment via a messaging bus, first events spawned by a first processor managing an object store accessible via the cloud environment. The method also includes receiving, at the centralized index, second events spawned by a second processor managing an archival store accessible via the cloud environment. Also, the method includes normalizing metadata in the first and second events. Moreover, the method includes indexing and storing the metadata in the centralized index. The object store and the archival store operate in different object subsystems of the cloud environment. Other systems, methods, and computer program products are described for managing import, export, and copy of data within a public tiered object storage environment in accordance with more embodiments.

Abstract: A method for pre-fetching objects from an object store and storing them in an object cache is disclosed. In one embodiment, such a method includes monitoring access to objects in an object store. The objects have metadata tags, such as user-defined or pre-defined metadata tags, associated therewith. The method further maintains a database for metadata tags associated with the objects, and keeps track of a number of object accesses per metadata tag. In the event a threshold number of object accesses for a particular metadata tag is reached, the method promotes objects having the metadata tag and/or a related metadata tag to an object cache. A corresponding system and computer program product are also disclosed.

Abstract: Exemplary embodiments for increased in-line deduplication efficiency in a computing environment are provided. Embodiments include incrementing the size of data samples from fixed size data chunks for each nth iteration for reaching a full size of an object requested for in-line deduplication, calculating in nth iterations hash values on data samples from fixed size data chunks extracted from the object, and matching in a nth hash index table the calculated nth iteration hash values for the data samples from the fixed size data chunks with a corresponding hash value of existing objects in storage, wherein the nth hash index table is built for each nth iteration of the data samples belonging to the fixed data chunks.

Abstract: A method for pre-fetching objects from an object store and storing them in an object cache is disclosed. In one embodiment, such a method includes monitoring access to objects in an object store. The objects have metadata tags, such as user-defined or pre-defined metadata tags, associated therewith. The method further maintains a database for metadata tags associated with the objects, and keeps track of a number of object accesses per metadata tag. In the event a threshold number of object accesses for a particular metadata tag is reached, the method promotes objects having the metadata tag and/or a related metadata tag to an object cache. A corresponding system and computer program product are also disclosed.

Abstract: Exemplary embodiments for increased in-line deduplication efficiency in a computing environment are provided. Embodiments include incrementing the size of data samples from fixed size data chunks for each nth iteration for reaching a full size of an object requested for in-line deduplication, calculating in nth iterations hash values on data samples from fixed size data chunks extracted from the object, and matching in a nth hash index table the calculated nth iteration hash values for the data samples from the fixed size data chunks with a corresponding hash value of existing objects in storage, wherein the nth hash index table is built for each nth iteration of the data samples belonging to the fixed data chunks.

Abstract: Exemplary method, system, and computer program product embodiments for increased in-line deduplication efficiency in a computing environment are provided. In one embodiment, by way of example only hash values are calculated in nth iterations for accumulative data chunks extracted from an object requested for in-line deduplication. For each of the nth iterations, the calculated hash values for the accumulative data chunks are matched in a nth hash index table with a corresponding hash value of existing objects in storage. The nth hash index table is exited upon detecting a mismatch during the matching. The mismatch is determined to be a unique object and is stored. A hash value for the object is calculated. A master hash index table is updated with the calculated hash value for the object and the calculated hash values for the unique object. Additional system and computer program product embodiments are disclosed and provide related advantages.

Abstract: Exemplary embodiments for increased in-line deduplication efficiency in a computing environment are provided. In one embodiment, by way of example only, hash values are calculated in nth iterations on data samples from fixed size data chunks extracted from an object requested for in-line deduplication. For each of the nth iterations, the calculated hash values for the data samples from the fixed size data chunks are matched in an nth hash index table with a corresponding hash value of existing objects in storage. The nth hash index table is exited upon detecting a mismatch during the matching. The mismatch is determined to be a unique object and is stored. A hash value for the object is calculated. A master hash index table is updated with the calculated hash value for the object and the calculated hash values for the unique object.