Reinforcement learning-based SDN routing scheme empowered by causality detection and GNN

IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Frontiers in Computational Neuroscience Pub Date : 2024-04-12 DOI:10.3389/fncom.2024.1393025
Yuanhao He, Geyang Xiao, Jun Zhu, Tao Zou, Yuan Liang
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Abstract

In recent years, with the rapid development of network applications and the increasing demand for high-quality network service, quality-of-service (QoS) routing has emerged as a critical network technology. The application of machine learning techniques, particularly reinforcement learning and graph neural network, has garnered significant attention in addressing this problem. However, existing reinforcement learning methods lack research on the causal impact of agent actions on the interactive environment, and graph neural network fail to effectively represent link features, which are pivotal for routing optimization. Therefore, this study quantifies the causal influence between the intelligent agent and the interactive environment based on causal inference techniques, aiming to guide the intelligent agent in improving the efficiency of exploring the action space. Simultaneously, graph neural network is employed to embed node and link features, and a reward function is designed that comprehensively considers network performance metrics and causality relevance. A centralized reinforcement learning method is proposed to effectively achieve QoS-aware routing in Software-Defined Networking (SDN). Finally, experiments are conducted in a network simulation environment, and metrics such as packet loss, delay, and throughput all outperform the baseline.
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基于因果关系检测和 GNN 的强化学习 SDN 路由方案
近年来,随着网络应用的迅猛发展和人们对高质量网络服务需求的不断增长,服务质量(QoS)路由已成为一项关键的网络技术。机器学习技术,尤其是强化学习和图神经网络的应用,在解决这一问题方面引起了广泛关注。然而,现有的强化学习方法缺乏对代理行为对交互环境的因果影响的研究,图神经网络也不能有效地表示链路特征,而链路特征对路由优化至关重要。因此,本研究基于因果推理技术量化智能代理与交互环境之间的因果影响,旨在引导智能代理提高探索行动空间的效率。同时,采用图神经网络嵌入节点和链接特征,并设计了综合考虑网络性能指标和因果相关性的奖励函数。提出了一种集中强化学习方法,以有效实现软件定义网络(SDN)中的 QoS 感知路由。最后,在网络仿真环境中进行了实验,结果表明丢包、延迟和吞吐量等指标均优于基线。
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来源期刊
Frontiers in Computational Neuroscience
Frontiers in Computational Neuroscience MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
5.30
自引率
3.10%
发文量
166
审稿时长
6-12 weeks
期刊介绍: Frontiers in Computational Neuroscience is a first-tier electronic journal devoted to promoting theoretical modeling of brain function and fostering interdisciplinary interactions between theoretical and experimental neuroscience. Progress in understanding the amazing capabilities of the brain is still limited, and we believe that it will only come with deep theoretical thinking and mutually stimulating cooperation between different disciplines and approaches. We therefore invite original contributions on a wide range of topics that present the fruits of such cooperation, or provide stimuli for future alliances. We aim to provide an interactive forum for cutting-edge theoretical studies of the nervous system, and for promulgating the best theoretical research to the broader neuroscience community. Models of all styles and at all levels are welcome, from biophysically motivated realistic simulations of neurons and synapses to high-level abstract models of inference and decision making. While the journal is primarily focused on theoretically based and driven research, we welcome experimental studies that validate and test theoretical conclusions. Also: comp neuro
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