1. A computer-implemented method, comprising: determining a plurality of virtual workload deployment requests that were processed within one or more data centers during a historical window of time, the one or more data centers having a plurality of physical servers; classifying each of the plurality of virtual workload deployment requests into one of a plurality of categories; generating, for each of the plurality of categories and by operation of one or more computer processors, a respective neural network prediction model, based on the virtual workload deployment requests classified into the respective category, comprising, for each of the plurality of categories: determining a number of input neurons of the neural network prediction mode, where the number of input neurons is determined based on recent workload variations to be considered in accurately predicting future virtual workload deployment demands of the one or more data centers; determining a number of hidden neurons to include within the neural network prediction model for each of the plurality of categories; separating the plurality of virtual workload deployment requests classified into the respective category into a plurality of discretized time slots; and processing virtual workload deployment requests in each of the plurality of discretized time slots using a respective one of a plurality of input neurons of a neural network; and determining a number of physical servers of the plurality of physical servers to have active at a future moment in time, based on a predicted number of virtual workload deployment requests calculated for each of the plurality of categories using the generated neural network prediction models.