Abstract: Systems and methods for dynamic and automatic data storage scheme switching in a distributed data storage system. A machine learning-based policy for computing probable future content item access patterns based on historical content item access patterns is employed to dynamically and automatically switch the storage of content items (e.g., files, digital data, photos, text, audio, video, streaming content, cloud documents, etc.) between different data storage schemes. The different data storage schemes may have different data storage cost and different data access cost characteristics. For example, the different data storage schemes may encompass different types of data storage devices, different data compression schemes, and/or different data redundancy schemes.

Abstract: Systems and methods for dynamic and automatic data storage scheme switching in a distributed data storage system. A machine learning-based policy for computing probable future content item access patterns based on historical content item access patterns is employed to dynamically and automatically switch the storage of content items (e.g., files, digital data, photos, text, audio, video, streaming content, cloud documents, etc.) between different data storage schemes. The different data storage schemes may have different data storage cost and different data access cost characteristics. For example, the different data storage schemes may encompass different types of data storage devices, different data compression schemes, and/or different data redundancy schemes.

Abstract: Lossless compression of a content item using a neural network trained on content item cohorts. A computing system includes a neural network that is used to train a plurality of symbol prediction models. Each symbol prediction model is trained based on a corresponding cohort of content items. A particular symbol prediction model of the models trained is selected based on an intrinsic characteristic of a particular content item to be losslessly compressed such as, for example, the type or file extension of the content item. The content item is then losslessly compressed based on a set of symbol predictions fed to an arithmetic coder that are generated using the particular symbol prediction model selected.

Abstract: Techniques for general-purpose lossless data compression using a neural network including compressing an original content item to a baseline lossless compressed data format. The baseline lossless compressed data format is binarized to a binarized format. The binarized format is arithmetically coded based on probability estimates from a neural network probability estimator. The neural network probability estimator generates the probability estimates for current symbols of the binarized format to be arithmetically coded based on symbols of the binarized format that have already been arithmetically coded.