SELF ADAPTIVE WORKLOAD CLASSIFICATION AND FORECASTING IN MULTI-TIERED STORAGE SYSTEM USING ARIMA TIME SERIES MODELING

Techniques are described data storage optimization that determine predicted values for I/O statistics using an ARIMA (auto-regressive integrated moving average) model. The ARIMA model may be used to capture periodic patterns and trends of workload I/O access to predict the future load demand. A current set of I/O statistics is collected for a current time period T. Using the current set and one or more ARIMA models, a predicted set of I/O statistics is determined for a next time period T+1. Each of the ARIMA models is characterized by model parameters including P denoting a number of auto-regressive terms, D denoting a number of nonseasonal difference needed for stationarity, and Q denoting a number of lagged forecast errors of prediction. A data storage optimizer may determine one or more data portions for movement from a current storage tier to a target storage tier using the predicted set of I/O statistics.