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RL-PMO:一种基于强化学习的优化算法,用于并行SFC迁移
Hefei Hu1, Zining Liu1, Fan Wu1
1School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Sensors (Basel, Switzerland)
|January 10, 2026
概括
本研究介绍了一种离线增强学习算法,用于边缘网络中多个虚拟网络函数 (VNF) 的并行迁移. RL-PMO方法实现了高成功率和更好的性能,特别是在高负载下.
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:
- 边缘网络因硬件故障和资源限制而面临中断.
- 服务功能链 (SFC) 易受节点故障的影响,需要有效的迁移策略.
- 在有限的资源下同时迁移多个虚拟网络功能 (VNF) 是一个重大挑战.
研究的目的:
- 提出一个高效可靠的算法,用于边缘网络中多个VNF的并行迁移.
- 为了应对影响SFCs的资源限制和节点故障的挑战.
- 优化VNF迁移流程以提高网络弹性.
主要方法:
- 开发了一个基于线下强化学习的平行迁移优化算法 (RL-PMO).
- 采用了两阶段的框架:用于生成迁移轨迹的启发式算法和用于政策网络培训的Decision Mamba模型.
- 利用双关键架构和CQL规范化来缓解分布转移和Q值高估.
主要成果:
- 在各种负载条件下,RL-PMO实现了大约95%的迁移成功率.
- 与传统的线下RL算法相比,在低/中等负载下表现出~13%的性能改善,在高负载下高达17%.
- 通过使用Decision Mamba模型,有效地捕捉了VNF和基础资源之间的依赖关系.
结论:
- 在节点故障期间,RL-PMO为SFC迁移提供了一种高效,可靠和资源意识的解决方案.
- 拟议的算法增强了边缘网络的稳定性和性能.
- 线下强化学习与Decision Mamba等先进模型相结合,对复杂的网络管理任务具有前景.
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