MACHINE LEARNING SYSTEMS AND METHODS FOR DATA PLACEMENT IN DISTRIBUTED STORAGE

A method of determining a primary storage location for a data record in a distributed system comprising a plurality of data stores physically located in corresponding geographic locations, includes initialising a machine learning mapping model using topology information of the distributed system, and determining a set of training feature vectors derived from metadata values associated with prior location requests. The model is trained using the training feature vectors and a corresponding set of target primary storage locations. A location request that includes a plurality of metadata values and is associated with a data record is received, and the metadata values are processed to determine a prediction feature vector comprising a plurality of prediction feature values. The model is executed using the prediction feature vector to identify one data store of the plurality of data stores as the primary storage location for the data record associated with the location request.