A technique for managing data objects in a storage cluster includes splitting a media data object into multiple portions at boundaries within the media data object. The technique further includes transforming the portions of the media data object into segments that provide individually processable units and distributing the segments among multiple computing nodes of the storage cluster for storage therein.

A technique for managing data objects in a storage cluster includes splitting a media data object into multiple portions at boundaries within the media data object. The technique further includes transforming the portions of the media data object into segments that provide individually processable units and distributing the segments among multiple computing nodes of the storage cluster for storage therein.

The disclosed computer-implemented method includes accessing cluster hardware information that identifies at least two different types of storage media within a cluster and provides an indication of a respective amount of data throughput for each identified type of storage media. The method next includes accessing popularity information for digital content that is to be stored in the cluster. The popularity information indicates how often the digital content is predicted to be accessed over a specified future period of time. The method also includes allocating the digital content on the different types of storage media within the cluster according to the popularity information. Accordingly, digital content predicted to have higher popularity is placed on storage media types with higher throughput amounts, and digital content predicted to have lower popularity is placed on storage media types with lower throughput amounts. Various other methods, systems, and computer-readable media are also disclosed.

The disclosed computer-implemented method includes accessing cluster hardware information that identifies at least two different types of storage media within a cluster and provides an indication of a respective amount of data throughput for each identified type of storage media. The method next includes accessing popularity information for digital content that is to be stored in the cluster. The popularity information indicates how often the digital content is predicted to be accessed over a specified future period of time. The method also includes allocating the digital content on the different types of storage media within the cluster according to the popularity information. Accordingly, digital content predicted to have higher popularity is placed on storage media types with higher throughput amounts, and digital content predicted to have lower popularity is placed on storage media types with lower throughput amounts. Various other methods, systems, and computer-readable media are also disclosed.

The disclosed computer-implemented method includes accessing cluster hardware information that identifies at least two different types of storage media within a cluster and provides an indication of a respective amount of data throughput for each identified type of storage media. The method next includes accessing popularity information for digital content that is to be stored in the cluster. The popularity information indicates how often the digital content is predicted to be accessed over a specified future period of time. The method also includes allocating the digital content on the different types of storage media within the cluster according to the popularity information. Accordingly, digital content predicted to have higher popularity is placed on storage media types with higher throughput amounts, and digital content predicted to have lower popularity is placed on storage media types with lower throughput amounts. Various other methods, systems, and computer-readable media are also disclosed.

The disclosed computer-implemented method includes accessing cluster hardware information that identifies at least two different types of storage media within a cluster and provides an indication of a respective amount of data throughput for each identified type of storage media. The method next includes accessing popularity information for digital content that is to be stored in the cluster. The popularity information indicates how often the digital content is predicted to be accessed over a specified future period of time. The method also includes allocating the digital content on the different types of storage media within the cluster according to the popularity information. Accordingly, digital content predicted to have higher popularity is placed on storage media types with higher throughput amounts, and digital content predicted to have lower popularity is placed on storage media types with lower throughput amounts. Various other methods, systems, and computer-readable media are also disclosed.

Systems, apparatuses, methods, and software for using a network to efficiently distribute media content assets from a virtually unlimited content library and/or other storage to a plurality of client devices, as well as bi-directional local content sharing between head ends, and dynamic distribution and generation of media content assets within the network.

Systems, apparatuses, methods, and software for using a network to efficiently distribute media content assets from a virtually unlimited content library and/or other storage to a plurality of client devices, as well as bi-directional local content sharing between head ends, and dynamic distribution and generation of media content assets within the network.