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Dynamic Switch Migration Algorithm with Q-learning towards Scalable SDN Control Plane

机译:Q-Learning对可扩展SDN控制平面的动态交换迁移算法

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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:多个控制器彼此连接以构造可伸缩的SDN(软件定义的网络)控制平面,并且每个开关连接到数据平面中的专用控制器。控制器上的负载根据交换机中变化的服务动态变化;并且需要从主控制器迁移到从控制器,以使负载适用于SDN控制器。在本文中提出了一种具有Q-Learning的动态交换机迁移算法,在本文中提出了一种模型切换迁移问题,基于交换机迁移模型重新定义Q学的参数,并随着更换负载动态获得开关迁移的决定在使用SDN中所提出的算法的控制器上。

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