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为MEC中的多种物联网用户进行实用驱动的端到端网络切片:多代理深度增强学习方法

Muhammad Asim Ejaz¹, Guowei Wu¹, Adeel Ahmed²

¹School of Software Technology, Dalian University of Technology, Dalian 116024, China.

Sensors (Basel, Switzerland)

|September 14, 2024

概括

本研究介绍了一种新的网络切片方法,使用多代理软演员关键算法来优化移动边缘计算 (MEC) 的资源配置. 该方法有效地管理资源,降低成本和执行时间,提高服务质量.

科学领域:

计算机科学计算机科学
电信工程电信工程电信工程
人工智能的人工智能

背景情况:

移动边缘计算 (MEC) 通过减少延迟来提高服务质量 (QoS).
为MEC部署云端面临网络切片方面的挑战,原因是流量不均和资源需求多样化.
高效的资源配置和请求类型的预测对于MEC性能至关重要.

研究的目的:

为移动边缘计算开发一种智能网络切片方法.
优化资源分配和效用在MEC环境中,需求波动.
为应对管理分布式云小组和网络切片的挑战.

主要方法:

利用T分布来预测常见和紧急的用户请求.
制定了多个代理马尔科夫决策过程 (MDP) 的问题.
引入了一个多代理软演员-关键 (MAgSAC) 算法,用于智能虚拟网络功能 (VNF) 实例管理.

主要成果:

该MAgSAC算法优化了30%的整体效用.
在能源消耗成本方面实现了12.4%的降低.
与MAA3C基准相比,执行时间缩短了21.7%.

结论:

拟议的MAgSAC方法有效地平衡了资源分配,收入,能源成本和延迟.
智能VNF实例的激活/禁用可以防止MEC中的资源浪费.
MAgSAC为边缘计算环境中的网络切片优化提供了卓越的解决方案.

关键词:

物联网 (IoT) 的物联网 (IoT) 的物联网.深度强化学习 (DRL) 是一种深度强化学习.从端到端的网络切片.移动边缘计算 (MEC) 是指移动边缘计算.多个代理的多个代理.公用事业优化优化

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