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The increasing popularity of cloud services today has led to a tremendous increase in energy consumption of data centers. As energy costs represent a significant part of the total cost of data center operations, energy cost reductions have become paramount for cloud providers to keep cloud services at an affordable price. This book provides insights into how to achieve significant energy cost reductions in environments with multiple interconnected data centers which feature connections to different power markets. The first part discusses various energy price forecasting methods in order to find the best method for predicting energy prices in power markets. The second part introduces a framework for cloud simulation in which different cloud conditions on real energy price data are tested. In addition, energy price forecasting methods are applied to further optimize resource scheduling and to maximize energy cost reductions. Finally, the results are summarized and the best model is presented resulting from cloud simulations over a wide range of energy price data.

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Andreas Egger, M.Sc.: Study of Computer Science at Vienna University of Technology. Ph.D. Student at the Institute for Software Technology and Interactive Systems, Vienna University of Technology.

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