  A novel cloud data center (DC) model is studied here with cognitive
capabilities for real-time (or online) flow compared to the batch tasks. Here,
a DC can determine the cost of using resources and an online user or the user
with batch tasks may decide whether or not to pay for getting the services. The
online service tasks have a higher priority in getting the service over batch
tasks. Both types of tasks need a certain number of virtual machines (VM). By
targeting on the maximization of total discounted reward, an optimal policy for
admitting task tasks is finally verified to be a state-related control limit
policy. Next, a lower and an upper bound for such an optimal policy are
derived, respectively, for the estimation and utilization in reality. Finally,
a comprehensive set of experiments on the various cases to validate this
proposed model and the solution is conducted. As a demonstration, the machine
learning method is adopted to show how to obtain the optimal values by using a
feed-forward neural network model. The results achieved in this paper will be
expectedly utilized in various cloud data centers with cognitive
characteristics in an economically optimal strategy.
