通过深度学习和软件定义网络来管理具有多连接性的设备的能源消耗
Ramiza Shams1, Atef Abdrabou1, Mohammad Al Bataineh1,2
1Department of Electrical and Communication Engineering, College of Engineering, United Arab Emirates University, Al-Ain P.O. Box 15551, Abu Dhabi, United Arab Emirates.
Sensors (Basel, Switzerland)
|September 28, 2023
概括
本研究介绍了一种使用软件定义网络和深度神经网络来管理多连接设备的能源消耗的新方法. 这种方法有效地减少了电力消耗,同时提高了5G和超越无线网络的网络性能.
科学领域:
- 计算机科学 计算机科学
- 电气工程 电气工程
- 电信 电信服务 电信服务 电信服务
背景情况:
- 多连接性使同时连接到多个无线电接入技术 (5G,4G LTE,WiFi) 成为可能,这对于满足不断增长的移动数据需求至关重要.
- 多路径TCP (MPTCP) 便于在这些多样化的链路上可靠的数据传输,但增加了电池驱动设备的能源消耗.
- 在多家无线设备中管理能效是当前和未来移动网络面临的重大挑战.
研究的目的:
- 开发和评估使用MPTCP的多连接设备的能源管理策略.
- 利用软件定义网络 (SDN) 和深度神经网络 (DNN) 来优化能源消耗.
- 为了提高网络吞吐量性能,并节省能源.
主要方法:
- 在SDN控制器上实现两个轻量级算法,用于管理多连接.
- 使用一个硬件测试台与双主机无线节点连接到WiFi和蜂网络.
- 使用在各种网络场景中训练有素的DNN来改进网络连接决策.
主要成果:
- 拟议的基于SDN和DNN的方法显著降低了设备的能耗.
- 与单路TCP和标准MPTCP算法 (Cubic,BALIA) 相比,该方法实现了改进的网络吞吐量性能.
- 实验验证证明了算法的有效性在现实世界双主机网络环境中.
结论:
- SDN和DNN集成为管理多连接设备的能源消耗提供了有效的解决方案.
- 开发的算法为优化5G和未来无线网络资源利用提供了实用方法.
- 这种方法平衡了高性能需求与移动设备能源效率的关键要求.
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