Abstract: Examples disclosed herein relate to systems and methods for deep learning enhanced garbage collection. Disclosed methods may include receiving, at a controller of an array, a time series dataset including a number of writes and a number of deletes; generating, by the controller, a tensor by encoding the time series data using a plurality of frequencies; determining a pattern in the time series data; generating a forecast of deletes based on the pattern; determining, based on the forecast, a number of cumulative deletes to the array at a predetermined time; at the predetermined time, comparing the forecasted number of cumulative deletes with a number of actual deletes to the array; and based on the comparison, scheduling a garbage collection (GC) algorithm to run on the array.

Abstract: Examples disclosed herein relate to systems and methods for deep learning enhanced garbage collection. Disclosed methods may include receiving, at a controller of an array, a time series dataset including a number of writes and a number of deletes; generating, by the controller, a tensor by encoding the time series data using a plurality of frequencies; determining a pattern in the time series data; generating a forecast of deletes based on the pattern; determining, based on the forecast, a number of cumulative deletes to the array at a predetermined time; at the predetermined time, comparing the forecasted number of cumulative deletes with a number of actual deletes to the array; and based on the comparison, scheduling a garbage collection (GC) algorithm to run on the array.