Storage Capacity Prediction using Population Analytics

Abstract

Capacity forecasting is a common feature in popular storage systems. The storage system vendors use prediction methods based on the historical usage for forecasting future utilization. However, we observed that such methods do not perform well under situations when the storage usage may not follow the historical trend. To address the problem, we propose a prediction method based on the data observed over 18,000 storage system. An analysis of a large scale dataset allows us to identify the popular trends across the storage systems at various stages of their lifecycle and further improve the prediction. Our evaluation shows that using the population data reduces the prediction error by 45% over linear regression.