Abstract: A method for storing data in a system that includes a plurality of storage devices, the method that includes obtaining object usage data from the plurality of storage devices, determining, using the object usage data, object clusters, where at least one object cluster of the object clusters includes at least two objects that are associated based on access patterns, migrate a first object, of the two objects, from a first storage device of the plurality of storage devices to a second storage device of the plurality of storage devices.

Abstract: A method for storing data in a system that includes a plurality of storage devices, the method that includes obtaining object usage data from the plurality of storage devices, determining, using the object usage data, object clusters, where at least one object cluster of the object clusters includes at least two objects that are associated based on access patterns, migrate a first object, of the two objects, from a first storage device of the plurality of storage devices to a second storage device of the plurality of storage devices.

Abstract: A method, computer program product, and computing system for partitioning an address space of a storage object of a log-structured file system into a plurality of slices, wherein the log-structured file system includes a plurality of storage objects in a plurality of storage tiers. One or more physical data blocks of the storage object may be allocated to each of the plurality of slices. A read temperature associated with at least one slice of the plurality of slices may be determined. A read temperature associated with each physical data block allocated to the at least one slice may be determined. At least one physical data block allocated to the at least one slice may be retiered between the plurality of storage tiers based upon, at least in part, the read temperature associated with each physical data block of the one or more physical data blocks allocated to the at least one slice.

Abstract: A method, computer program product, and computing system for partitioning an address space of a storage object of a log-structured file system into a plurality of slices, wherein the log-structured file system includes a plurality of storage objects in a plurality of storage tiers. One or more physical data blocks of the storage object may be allocated to each of the plurality of slices. A read temperature associated with at least one slice of the plurality of slices may be determined. A read temperature associated with each physical data block allocated to the at least one slice may be determined. At least one physical data block allocated to the at least one slice may be retiered between the plurality of storage tiers based upon, at least in part, the read temperature associated with each physical data block of the one or more physical data blocks allocated to the at least one slice.

Abstract: Implementations are provided herein for systems, methods, and a non-transitory computer product configured to analyze an input/output (IO) pattern for a data storage system, to identify an application type based on the IO pattern, and to select optimal deduplication and compression configurations based on the application type. The teachings herein facilitate machine learning of various metrics and the interrelations between these metrics, such as past IO patterns, application types, deduplication configurations, compression configurations, and overall system performance. These metrics and interrelations can be stored in a data lake. In some embodiments, data objects can be segmented in order to optimize configurations with more granularity. In additional embodiments, predictive techniques are used to select deduplication and compression configurations.

Abstract: Implementations are provided herein for systems, methods, and a non-transitory computer product configured to analyze an input/output (IO) pattern for a data storage system, to identify an application type based on the IO pattern, and to select optimal deduplication and compression configurations based on the application type. The teachings herein facilitate machine learning of various metrics and the interrelations between these metrics, such as past IO patterns, application types, deduplication configurations, compression configurations, and overall system performance. These metrics and interrelations can be stored in a data lake. In some embodiments, data objects can be segmented in order to optimize configurations with more granularity. In additional embodiments, predictive techniques are used to select deduplication and compression configurations.

Abstract: Implementations are provided herein for systems, methods, and a non-transitory computer product configured to analyze an input/output (IO) pattern for a data storage system, to identify an application type based on the IO pattern, and to select optimal deduplication and compression configurations based on the application type. The teachings herein facilitate machine learning of various metrics and the interrelations between these metrics, such as past IO patterns, application types, deduplication configurations, compression configurations, and overall system performance. These metrics and interrelations can be stored in a data lake. In some embodiments, data objects can be segmented in order to optimize configurations with more granularity. In additional embodiments, predictive techniques are used to select deduplication and compression configurations.

Abstract: Implementations are provided herein for systems, methods, and a non-transitory computer product configured to analyze an input/output (IO) pattern for a data storage system, to identify an application type based on the IO pattern, and to select optimal deduplication and compression configurations based on the application type. The teachings herein facilitate machine learning of various metrics and the interrelations between these metrics, such as past IO patterns, application types, deduplication configurations, compression configurations, and overall system performance. These metrics and interrelations can be stored in a data lake. In some embodiments, data objects can be segmented in order to optimize configurations with more granularity. In additional embodiments, predictive techniques are used to select deduplication and compression configurations when certain regarding an application type is lacking.