Forecasting backup storage consumption

Blaž Koncilja (2016) Forecasting backup storage consumption. EngD thesis.

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Abstract

Storage needs for archiving data are increasing. Companies need to store more and more data to function normally. Storing this data can be costly, that is why we want to provide sufficient storage capacity to meet the demands and not exceed them which brings additional costs. With the help of data mining we are trying to forecast trends in storage consumption. We acquired data from two environments for archiving and saved them to a database. We analysed data consumption trends with linear regression, piecewise linear regression and k-nearest neighbours. Piecewise linear regression proved to be the most accurate and reliable. Even though results are good enough to be implemented into production, we should be cautious as the two environments have different characteristics and this influences the forecasting.

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