  In the context of capacity planning, forecasting the evolution of informatics
servers usage enables companies to better manage their computational resources.
We address this problem by collecting key indicator time series and propose to
forecast their evolution a day-ahead. Our method assumes that data is
structured by a daily seasonality, but also that there is typical evolution of
indicators within a day. Then, it uses the combination of a clustering
algorithm and Markov Models to produce day-ahead forecasts. Our experiments on
real datasets show that the data satisfies our assumption and that, in the case
study, our method outperforms classical approaches (AR, Holt-Winters).
