A majority of traffic engineering techniques aims to minimize network congestion in SDN, but they commonly have considered some aspects of the flows and their demands for evaluating applications in SDN. This paper focuses on a scalable traffic engineering scheme for efficiently mapping traffic demands to paths in SDN datacenters. In this regard, an optimal solution is introduced to schedule elephant flows across paths to mitigate network congestion and increase load balancing in SDN. Nevertheless, optimal flows scheduling mechanism is still not applicable because the required time to calculate the optimal paths is not tolerable in SDN. We present a new decomposition technique for limiting the search space based on the linear programming method to solve the problem in a reasonable time. Since the mice flows have the sensitive time requirements, some paths are reserved for immediately forwarding these flows. The proposed method achieves reasonable performance regarding computation time compared with state-of-the-art solutions devoted to this topic. The results also show that the proposed traffic engineering method is effective to control network congestion and provide load balancing in SDN datacenters.

大多数流量工程技术旨在最大程度地减少SDN中的网络拥塞，但他们通常考虑了流量的某些方面及其对SDN中的应用程序评估的需求。本文重点介绍可扩展的流量工程方案，以有效地将流量需求映射到SDN数据中心中的路径。在这方面，引入了一种最佳解决方案来调度路径中的大象流，以减轻网络拥塞并增加SDN中的负载平衡。但是，最佳流调度机制仍然不适用，因为计算最佳路径所需的时间在SDN中是无法忍受的。我们提出了一种新的用于限制搜索空间的分解技术，该技术基于线性规划方法可以在合理的时间内解决该问题。由于老鼠的流量具有敏感的时间要求，保留一些路径以立即转发这些流。与专门针对此主题的最新解决方案相比，该方法在计算时间方面实现了合理的性能。结果还表明，所提出的流量工程方法可有效控制SDN数据中心的网络拥塞并提供负载平衡。