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Automatic guarantee scheme for intent-driven network slicing and reconfiguration
Journal of Network and Computer Applications ( IF 8.7 ) Pub Date : 2021-07-14 , DOI: 10.1016/j.jnca.2021.103163
Hui Yang 1 , Kaixuan Zhan 1 , Bowen Bao 1 , Qiuyan Yao 1 , Jie Zhang 1 , Mohamed Cheriet 2
Affiliation  

Commonly, the network configuration leans upon the operators’ experience to operate network, including command-line configuration, middle-ware scripts, and troubleshooting. However, with the rise of neoteric B5G services, the manual way lacks flexibility and timeliness, resulting in an unsatisfactory level of configuration. It is necessary to consider a manual free configuration way for transport network. To cope with this problem, we present an intent-driven network architecture with self-adapting slicing policy and slices reconfiguration in an intent-orient manner. Aiming at intent request, intent analysis based on latent dirichlet allocation is introduced to establish the semantic graph to comprehend and enact the required slicing configuration language, namely intent translation. Then, in line with intent translation, we propose a self-adapted slicing policy generation and optimization base on deep reinforcement learning (SPG-RL) to find combined strategies that meet the intent requirements by dynamically integrating fine-grained slicing policies. Finally, deep neural evolution network (DNEN)-assisted model (SPG-RL-DNEN) is introduced to locate the incompatible slices at the millisecond level for slicing reconfiguration. When the network entropy reaches the threshold, SPG-RL-DNEN would reconfigure the incompatible slices for intent guarantee. The efficiency of our proposal are verified on enhanced SDN testbed.



中文翻译:

意图驱动的网络切片和重配置的自动保障方案

通常,网络配置依赖于运营商操作网络的经验,包括命令行配置、中间件脚本和故障排除。但随着近代B5G业务的兴起,人工方式缺乏灵活性和及时性,导致配置水平不尽如人意。有必要考虑一种手动自由配置传输网络的方式。为了解决这个问题,我们提出了一个意图驱动的网络架构,它具有自适应切片策略,并以面向意图的方式重新配置切片。针对意图请求,引入基于潜在狄利克雷分配的意图分析来建立语义图以理解和制定所需的切片配置语言,即意图翻译。然后,根据意图翻译,我们提出了一种基于深度强化学习(SPG-RL)的自适应切片策略生成和优化,通过动态集成细粒度切片策略来寻找满足意图要求的组合策略。最后,引入深度神经进化网络(DNEN)辅助模型(SPG-RL-DNEN)以毫秒级定位不兼容切片进行切片重构。当网络熵达到阈值时,SPG-RL-DNEN 将重新配置不兼容的切片以保证意图。我们的提议的效率在增强的 SDN 测试平台上得到了验证。引入深度神经进化网络 (DNEN) 辅助模型 (SPG-RL-DNEN) 以在毫秒级别定位不兼容切片以进行切片重新配置。当网络熵达到阈值时,SPG-RL-DNEN 将重新配置不兼容的切片以保证意图。我们的提议的效率在增强的 SDN 测试平台上得到了验证。引入深度神经进化网络 (DNEN) 辅助模型 (SPG-RL-DNEN) 以在毫秒级别定位不兼容切片以进行切片重新配置。当网络熵达到阈值时,SPG-RL-DNEN 将重新配置不兼容的切片以保证意图。我们的提议的效率在增强的 SDN 测试平台上得到了验证。

更新日期:2021-07-14
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