In today’s world, enterprises are in dire need of using information technology systems to do their missions. This requires development of data centers using which, they can present existing rules, strategies, and structures in the technology context. Since then, it became clear that the accurate performance of the data centers leads to consistence, acceleration of access, and continuation of the business of enterprises, but despite of presenting relevant models, there are still obscurities in the identification of key influential variables. The purpose of this study is to present a dynamic model of data center networks using Venism software, in which 125 influential variables, relevant to 15 stock variables, are derived by reviewing the literature and 12 causal loops were identified. Then, the model is presented, as causal-loop diagrams and the stock-flow model and various scenarios such as enhancing training capacity and appeal, imposing overload on the network and switch failure are examined. Considering the results and the sensitivity analysis performed, it became clear that the appeal of the services, the skills attained, and the quality, as well as the bandwidth tolerance capacity, storing, and processing power of the data center has significant importance in identifying the level of performance of the data center.