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A novel cloud data center (DC) model is studied here with cognitivecapabilities 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 userwith batch tasks may decide whether or not to pay for getting the services. Theonline service tasks have a higher priority in getting the service over batchtasks. Both types of tasks need a certain number of virtual machines (VM). Bytargeting on the maximization of total discounted reward, an optimal policy foradmitting task tasks is finally verified to be a state-related control limitpolicy. Next, a lower and an upper bound for such an optimal policy arederived, respectively, for the estimation and utilization in reality. Finally,a comprehensive set of experiments on the various cases to validate thisproposed model and the solution is conducted. As a demonstration, the machinelearning method is adopted to show how to obtain the optimal values by using afeed-forward neural network model. The results achieved in this paper will beexpectedly utilized in various cloud data centers with cognitivecharacteristics in an economically optimal strategy.

Task Admission Control and Boundary Analysis of Cognitive Cloud Data Centers