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Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
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在MEC工作负载中部署DRL驱动的智能SFC,用于动态物联网网络.

Seyha Ros1, Intae Ryoo2, Seokhoon Kim1,3

  • 1Department of Software Convergence, Soonchunhyang University, Asan 31538, Republic of Korea.

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概括

本研究介绍了物联网 (IoT) 任务卸载和资源编排在多访问边缘计算 (MEC) 中的智能框架. 深度增强学习优化了资源配置,减少了延迟和能源消耗.

关键词:
深度强化学习的学习.物联网的物联网,就是物联网.多访问边缘计算边缘计算网络功能虚拟化 网络功能虚拟化服务功能链接服务功能链接

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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:

  • 物联网 (IoT) 传感器网络的扩散产生了大量的数据,需要有效的资源管理.
  • 在异质物联网网络中维持服务质量 (QoS) 受到有限的多访问边缘计算 (MEC) 资源和日益增长的任务卸载需求的挑战.
  • 网络拥堵,服务延迟和资源利用效率低下会降低物联网-MEC系统的性能.

研究的目的:

  • 为动态的物联网-MEC环境提出一个智能任务卸载和资源编排框架.
  • 为了优化能源消耗,计算成本,网络拥堵和服务延迟.
  • 提高整体系统效率,并实现边缘计算的最佳政策.

主要方法:

  • 开发了一个整合任务卸载和动态资源编排的框架.
  • 用户服务功能链 (SFC) 用于虚拟网络功能 (VNF) 的放置和路由路径的确定.
  • 利用深度强化学习 (DRL),特别是深度Q网络 (DQN),用于适应性资源配置和任务卸载决策.

主要成果:

  • 基于DRL的方案显著超过了参考方法.
  • 证明了服务延迟和能源消耗的大幅度减少.
  • 展示了交付,吞吐量和累积奖励方面的改进.

结论:

  • 拟议的智能框架有效地解决了物联网-MEC环境中的挑战.
  • DRL驱动的动态资源编排和任务卸载优化了系统性能.
  • 该方法为边缘计算中的资源管理提供了灵活和适应性的解决方案.