  Workload predictions in cloud computing is obviously an important topic. Most
of the existing publications employ various time series techniques, that might
be difficult to implement. We suggest here another route, which has already
been successfully used in financial engineering and photovoltaic energy. No
mathematical modeling and machine learning procedures are needed. Our computer
simulations via realistic data, which are quite convincing, show that a setting
mixing algebraic estimation techniques and the daily seasonality behaves much
better. An application to the computing resource allocation, via virtual
machines, is sketched out.
