MULTI-MODEL BLOCK CAPACITY FORECASTING FOR A DISTRIBUTED STORAGE SYSTEM

Systems and methods for use a multi-model block capacity forecasting approach are provided to predict when a distributed storage system will reach a fullness threshold. According to one embodiment, given a time series telemetry dataset collected from multiple distributed storage systems, a forecasting algorithm trains multiple time series forecasting models (e.g., Simple linear regression (SLR), Autoregressive Integrated Moving Average (ARIMA), Generalized additive model (GAM), and/or others) for each of the distributed storage systems. The best performing time series forecasting model is then independently selected for each of the distributed storage systems based on a respective performance metric (e.g., root mean squared error) associated with the time series forecasting models. Forecasted data points for each distributed storage system and the corresponding future time frames in which one or more predetermined or configurable block capacity fullness thresholds are predicted to be crossed may be determined based on the selected time series forecasting models.