Accurate forecasts of data center energy consumptions can help eliminate risks caused by underprovisioning
or waste caused by over-provisioning. However, due to nonlinearity and
complexity, energy prediction remains a challenge. An added layer of complexity further comes
from dynamically changing workloads. There is a lack of physical principle based clear-box
models, and existing black-box based methods such neural networks are restrictive. In this
paper, we develop an evolutionary neural network as a structurally optimal black-box model to
forecast the energy consumption of a dynamic cloud data center. In particular, the approach to
evolving an optimal network is developed from several novel mechanisms of a genetic
algorithm, such as a structurally-inclusive matrix encoding and species parallelism that help
maintain an overall increasing fitness to overcome slow convergence whilst preventing
premature dominance. The model is trained using part of the data obtained from a set of
MapReduce jobs on a 120-core Hadoop cluster and is then validated against unseen data. The
results, both in terms of prediction speed and accuracy, suggest that this evolutionary neural
network approach to cloud data center forecast is highly promising
