  This work proposes an automatic methodology for modeling complex systems. Our
methodology is based on the combination of Grammatical Evolution and classical
regression to obtain an optimal set of features that take part of a linear and
convex model. This technique provides both Feature Engineering and Symbolic
Regression in order to infer accurate models with no effort or designer's
expertise requirements. As advanced Cloud services are becoming mainstream, the
contribution of data centers in the overall power consumption of modern cities
is growing dramatically. These facilities consume from 10 to 100 times more
power per square foot than typical office buildings. Modeling the power
consumption for these infrastructures is crucial to anticipate the effects of
aggressive optimization policies, but accurate and fast power modeling is a
complex challenge for high-end servers not yet satisfied by analytical
approaches. For this case study, our methodology minimizes error in power
prediction. This work has been tested using real Cloud applications resulting
on an average error in power estimation of 3.98%. Our work improves the
possibilities of deriving Cloud energy efficient policies in Cloud data centers
being applicable to other computing environments with similar characteristics.
