Premonition of storage response class using Skyline ranked Ensemble method

Tertiary storage areas are integral parts of compute environment and are primarily used to store vast amount of data that is generated from any scientific/industry workload. Modelling the possible pattern of usage of storage area helps the administrators to take preventive actions and guide users on how to use the storage areas which are tending towards slower to unresponsive state. Treating the storage performance parameters as a time series data helps to predict the possible values for the next `n´ intervals using forecasting models like ARIMA. These predicted performance parameters are used to classify if the entire storage area or a logical component is tending towards unresponsiveness. Classification is performed using the proposed Skyline ranked Ensemble model with two possible classes, i.e. high response state and low response state. Heavy load scenarios were simulated and close to 95% of the behaviour were explained using the proposed model.