基于Docker的频谱智能监控应用微服务体系结构研究

Ding Wang, Jianyun Chen, Yongbin Zhou, Jinzhao She
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引用次数: 1

摘要

基于深度学习的频谱智能监测技术可以减少频谱监测中的人为干扰因素,显著提高频谱监测的实时性和准确性。然而,由于深度学习算法的复杂运行环境和深度学习框架的多样性,频谱监测应用的部署和移植更加困难。本文提出了一种基于docker的频谱智能监控应用微服务架构,该架构主要分为频谱监控资源层、频谱监控资源服务层和频谱监控资源服务层。采用Docker封装基于深度学习的频谱监控算法,采用Kubernetes进行统一编排部署,简化了频谱监控算法的部署和迁移,提高了频谱监控的效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on Spectrum Intelligent Monitoring Application Microservice Architecture Based on Docker
The spectrum intelligent monitoring technology based on deep learning can reduce the artificial interference factors of spectrum monitoring and significantly improve the real-time performance and accuracy of spectrum monitoring. However, due to the complex operating environment of deep learning algorithms and the variety of deep learning frameworks, the deployment and transplantation of spectrum monitoring applications are more difficult. This paper proposes a docker-based spectrum intelligent monitoring application microservice architecture, which is mainly divided into spectrum monitoring resource layer, spectrum monitoring resource service layer and spectrum monitoring resource service layer. Docker is used to encapsulate the spectrum monitoring algorithm based on deep learning, and Kubernetes is used for unified arrangement and deployment, which simplifies the deployment and migration of the spectrum monitoring algorithm and improves the efficiency of spectrum monitoring.
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