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Q-learning based adaptive subgraph generation algorithm for graph routing in ISA 100.11a

机译:ISA 100.11a中基于Q学习的图路由自适应子图生成算法

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Graph routing is a routing protocol opted to run in ISA 100.11a based networks. Attention towards proposing a time efficient subgraph is received by the researchers. While there can be a lot of network performance level factors considered to generate subgraphs for efficient and timely delivery of data. ISA 100.11a specification doesn't specifies a graph generation algorithm to generate subgraphs for a network. Hence, numerous efforts can be found where the research community has taken initiative in proposing subgraphs considering factors affecting the performance of the network. Subgraphs which are optimal at a particular instance of time may not prove to be effective after a certain time period. Therefore, there is a need to update the subgraphs in the network based on the history of the network performance parameters accumulated over a certain time period which will not necessarily regenerate the subgraphs in the network based on the history will penalize the subgraphs which degrade the performance of the applications using the subgraph. Q-learning mechanism is a lightweight mechanism which works on the idea of rewarding an action considered good for environment. We propose an idea of generating subgraphs after a time period based on Q-learning mechanism.
机译:图路由是一种选择在基于ISA 100.11a的网络中运行的路由协议。研究人员对提出一个省时的子图提出了建议。尽管可以考虑许多网络性能级别因素,以生成子图以高效,及时地传输数据。 ISA 100.11a规范未指定图生成算法来生成网络的子图。因此,在研究团体主动提出考虑影响网络性能的因素的子图的提议时,可以找到许多努力。在特定时间实例上最佳的子图在特定时间段后可能无法证明是有效的。因此,需要基于在一定时间段内累积的网络性能参数的历史来更新网络中的子图,这不一定会基于历史来重新生成网络中的子图,这将对降低性能的子图造成不利影响。使用子图的应用程序。 Q学习机制是一种轻量级的机制,其工作原理是奖励对环境有益的行为。我们提出了一种基于Q学习机制在一段时间后生成子图的想法。

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