在动态物联网-MEC网络中,基于用户移动性和资源需求预测的SFC主动重新配置
Shuang Guo1,2, Liang Liu3, Tengxiang Jing3
1Chongqing College of Mobile Communication, Qijiang, Chongqing, China.
PloS one
|August 1, 2024
概括
本研究介绍了一种用于在移动边缘计算网络中重新配置服务功能链的新策略. 基于预测的SFV主动重新配置算法通过预测用户移动性和资源需求来最大限度地减少延迟和成本.
科学领域:
- 计算机科学 计算机科学
- 网络化 网络化 网络化
- 移动计算 移动计算
背景情况:
- 具有多访问边缘计算 (MEC) 的移动物联网 (IoT) 网络依赖服务功能链 (SFC) 来实现安全和可扩展的流量传输.
- 在物联网-MEC环境中,用户移动性和动态网络流量导致SFC和分配资源之间的性能不匹配.
- 积极地重新配置SFC以适应网络变化是一个重大挑战.
研究的目的:
- 为物联网-MEC网络开发服务功能链重新配置策略 (SFC-RS).
- 通过预测用户流动性和资源需求,尽量减少端到端延迟和重新配置成本.
- 确保高质量的服务,尽管网络状态波动.
主要方法:
- 模拟SFC-RS作为一个整数线性编程 (ILP) 问题.
- 使用基于注意力的编码器移动方法开发用户轨迹预测模型.
- 设计一个使用长短期内存 (LSTM) 网络的VNF资源需求预测模型.
- 提出基于预测的SFV主动重新配置 (PSAR) 算法,以实现无的SFC迁移和路由更新.
主要成果:
- 与现有的算法 (TSRFCM,DDQ,OSA,DPSM) 相比,PSAR算法显著减少了端到端延迟.
- PSAR在降低重新配置成本方面表现出了显著的性能改善.
- 模拟结果验证了PSAR在保持网络一致性和服务质量的有效性.
结论:
- 拟议的PSAR算法有效地解决了在动态物联网-MEC网络中SFC重新配置的挑战.
- 用户移动性和资源需求的预测建模对于优化SFC性能至关重要.
- 在移动边缘计算环境中,PSAR提供了一种可靠的解决方案,以确保高质量的服务提供.
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