在使用DQL的软件定义网络中使用人工智能驱动的路由管道:一个迷你审查
Deepthi Goteti1, Vuyyuru Krishna Reddy1
1Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vijayawada, India.
Frontiers in artificial intelligence
|December 1, 2025
概括
深度Q学习 (DQL) 通过适应动态流量,提高吞吐量和减少延迟来增强数据中心网络路由. 然而,在生产部署方面,培训时间和可扩展性仍然存在挑战.
科学领域:
- 计算机科学 计算机科学
- 网络工程 网络工程
- 人工智能的人工智能
背景情况:
- 数据中心网络面临着动态流量的挑战,导致像延迟和拥堵这样的低效率.
- 传统的路由算法 (Dijkstra,ECMP) 在软件定义网络 (SDN) 中缺乏实时适应性.
- 强化学习 (RL) 提供了适应性,但存在可扩展性问题,导致了深度Q学习 (DQL) 的发展.
研究的目的:
- 对软件定义网络 (SDN) 的最新深度Q学习 (DQL) 方法进行审查和综合.
- 检查DQL架构,算法变体和仿真环境.
- 为 SDN 中的 DQL 提供结构化的分类和对权衡和部署问题的实际综合.
主要方法:
- 对最近应用到软件定义网络 (SDN) 的深度Q学习 (DQL) 方法的审查.
- 检查网络架构,算法变化和仿真环境,如Mininet与Ryu.
- 对经验性权衡的分析,重点关注吞吐量,延迟和趋同.
主要成果:
- 报告的研究表明,与ECMP相比,DQL可以提高15-22%的吞吐量,并减少10-12%的延迟时间.
- DQL使SDN控制器能够从使用神经网络的实时网络状态中学习路由策略.
- 权衡包括增加培训时间,推断延迟和持续的可扩展性挑战.
结论:
- DQL为SDN中的自适应路由提供了一个有希望的方法,提供了显著的性能改进.
- 目前的DQL实现在培训效率和可扩展性方面遇到了障碍,限制了即时的生产准备.
- 像联合学习,图形神经网络和可解释的人工智能等新兴方向是推动DQL用于实际SDN解决方案的关键.
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