This paper studies a cloud datacenter (DC) consisting of two types of tasks with different priority levels. While non-priority tasks generally request the use of a single virtual machine (VM), priority tasks may utilize multiple available VMs to accelerate processing. We focus on determining whether to accept or reject non-priority tasks to maximize overall system benefits. By formulating the problem as a stochastic dynamic program, it is verified that the best approach for handling non-priority tasks adheres to a control-limit framework. Both experimental outcomes and numerical evaluations highlight the efficacy of the proposed method, leading to the identification of the optimal threshold. The key contribution of this paper is the development of a stochastic dynamic program for DC resource management and the explicit derivation of an optimal control-limit policy. Both value iteration and linear programming methods are utilized to solve optimization problems. These results offer essential understanding for assessing the performance of various DC models, optimizing both rewards and resources efficiently.