Abstract: Embodiments disclosed herein relate to selective inline compression of data using data processing units (DPUs) of hosts running on the file system prior to storing the data on a remote storage system. To be compressed by the DPUs of the hosts, data should be not already compressed, the data should not subject to frequent read/write cycles off the remote storage system, and the DPU should have sufficient computing resources to compress the data. A file system is connected to the hosts and the remote storage. The file system provides a file heatmap that forecasts whether the data is frequently accessed. The file heatmap is created using machine learning models. After receiving the file heatmap, the host writes the data to the remote storage system and uses the file heatmap to determine if the data should be compressed.

Abstract: A method for managing data packing in a storage includes: receiving data from a computing device at a first point-in-time; analyzing the data to: determine characteristics of the data, and generate an extent for the data, in which the extent includes sequential tracks; analyzing sequential tracks to infer how a distribution of compression sizes change for the sequential tracks to obtain track-level in the compression sizes; forecasting, based on the characteristics of the data and the track-level changes in the compression sizes, a stability score of the distribution at a second point-in-time, in which the second point-in-time is after the first point-in-time; making a determination, based on the stability score, that the distribution would be the same in the second point-in-time; and placing, based on the determination, the tracks to a first zone in the storage, in which the storage further comprises a second zone and a third zone.