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Data center has recently emerged as a common infrastructure that includes numerous servers and host diversity of applications and cloud‐based services. In this paper, a dynamic method is proposed for traffic engineering in software‐defined data center networks helping to design an optimal demand‐path mapping with a tolerable computational complexity. To achieve the aim of this study, we used a linear programming to define the problem statement and then modeled the problem as a Path‐based multicommodity flow. To reduce time complexity, first, a decomposition technique is used to divide the problem model into smaller subproblems, which are then solved simultaneously by applying parallelizing techniques (ie, multiple‐core computing and OpenMP). Next, a suitable iterative algorithm is utilized to quickly converge subproblem solutions leading to an answer to the master problem. Simulation results show that solving time is considerably improved compared with a number of reported schemes devoted to this topic. In addition, implementing the proposed method serially yields an optimum solution, which takes significantly shorter time to reach than that of the generic (the LP model that is not decomposed) approach. In this paper, a dynamic method is proposed for traffic engineering in software‐defined data center networks helping to design an optimal demand‐path mapping with a tolerable computational complexity. To achieve the aim of this study, we used a linear programming to define the problem. To reduce time complexity, a decomposition technique is used to divide the problem model into smaller subproblems, which are then solved simultaneously by applying parallelizing techniques.