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Dynamic switch migration algorithm with Q-learning towards scalable SDN control plane

机译:通过Q学习向可扩展SDN控制平面进行动态交换机迁移的算法

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Multiple controllers are connected to each other to construct a scalable SDN (Software Defined Network) control plane, and every switch is connected to a dedicated controller in data plane. The load on controllers changes dynamically in accordance to the services varying in switches; and switches need to be migrated from their master controllers to slave controllers to make the load balanced for SDN controllers. A dynamic switch migration algorithm with Q-learning towards scalable SDN control plane is proposed in this paper, which models switch migration problem, redefines parameters of Q-learning based on the switch migration model, and gets decisions of switch migration dynamically with the changing load on controllers using the proposed algorithm in SDN.
机译:多个控制器相互连接,以构建可扩展的SDN(软件定义网络)控制平面,每个交换机均与数据平面中的专用控制器连接。控制器上的负载根据交换机中服务的变化而动态变化;并且需要将交换机从其主控制器迁移到从控制器,以平衡SDN控制器的负载。提出了一种面向可扩展SDN控制平面的Q学习的动态交换机迁移算法,该模型对交换机迁移问题进行建模,基于交换机迁移模型重新定义Q学习的参数,并随着负载的变化动态获取交换机迁移的决策。在控制器中使用提出的算法在SDN中。

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