  The Cloud paradigm is at a critical point in which the existing
energy-efficiency techniques are reaching a plateau, while the computing
resources demand at Data Center facilities continues to increase exponentially.
The main challenge in achieving a global energy efficiency strategy based on
Artificial Intelligence is that we need massive amounts of data to feed the
algorithms. This paper proposes a time-series data augmentation methodology
based on synthetic scenario forecasting within the Data Center. For this
purpose, we will implement a powerful generative algorithm: Generative
Adversarial Networks (GANs). Specifically, our work combines the disciplines of
GAN-based data augmentation and scenario forecasting, filling the gap in the
generation of synthetic data in DCs. Furthermore, we propose a methodology to
increase the variability and heterogeneity of the generated data by introducing
on-demand anomalies without additional effort or expert knowledge. We also
suggest the use of Kullback-Leibler Divergence and Mean Squared Error as new
metrics in the validation of synthetic time series generation, as they provide
a better overall comparison of multivariate data distributions. We validate our
approach using real data collected in an operating Data Center, successfully
generating synthetic data helpful for prediction and optimization models. Our
research will help optimize the energy consumed in Data Centers, although the
proposed methodology can be employed in any similar time-series-like problem.
