In this paper, we study the problem of data flow management in the presence of heterogeneous flows — elephant and mice flows — in software-defined networks (SDNs). Most of the researchers considered the homogeneous flows in SDN in the existing literature. The optimal data flow management in the presence of heterogeneous flows is NP-hard. Hence, we propose a game theory-based heterogeneous data flow management scheme, named FlowMan. In FlowMan, initially, we use a generalized Nash bargaining game to obtain a sub-optimal problem, which is NP-complete in nature. By solving it, we get the Pareto optimal solution for data-rate associated with each switch. Thereafter, we use a heuristic method to decide the flow-association with the switches, distributedly, which, in turn, helps to get a Pareto optimal solution. Extensive simulation results depict that FlowMan is capable of ensuring quality-of-service (QoS) for data flow management in the presence of heterogeneous flows. In particular, FlowMan is capable of reducing network delay by 77.8–98.7%, while ensuring 24.6–47.8% increase in network throughput, compared to the existing schemes such as FlowStat and CURE. Additionally, FlowMan ensures that per-flow delay is reduced by 27.7% with balanced load distribution among the SDN switches.

中文翻译：

---

FlowMan：软件定义网络中的 QoS 感知动态数据流管理

在本文中，我们研究了软件定义网络 (SDN) 中存在异构流（大象流和老鼠流）时的数据流管理问题。大多数研究人员在现有文献中考虑了 SDN 中的同构流。存在异构流时的最佳数据流管理是 NP 难的。因此，我们提出了一种基于博弈论的异构数据流管理方案，名为 FlowMan。在 FlowMan 中，最初，我们使用广义纳什讨价还价博弈来获得一个次优问题，该问题本质上是 NP 完全的。通过求解它，我们得到了与每个交换机相关的数据速率的帕累托最优解。此后，我们使用启发式方法来决定与交换机的流关联，分布式，这反过来有助于获得帕累托最优解。广泛的模拟结果表明 FlowMan 能够在异构流存在的情况下确保数据流管理的服务质量 (QoS)。特别是，与现有的 FlowStat 和 CURE 方案相比，FlowMan 能够将网络延迟降低 77.8-98.7%，同时确保网络吞吐量增加 24.6-47.8%。此外，FlowMan 通过在 SDN 交换机之间平衡负载分配，确保将每个流的延迟减少 27.7%。