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在移动边缘计算中延迟和能源意识的适应性服务迁移
Lina Li1, Junan Lv1, Shuxin Wang1
1School of Computer Science and Technology, Changchun University, Changchun, 130022, China.
Scientific reports
|January 24, 2026
概括
移动边缘计算 (MEC) 服务迁移平衡了延迟和能源使用. 一个新的深度Q网络模型 (NPER-D3QN) 优化了这些因素,以获得更好的用户体验和更低的运营成本.
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:
- 移动边缘计算 (MEC) 通过部署边缘服务器来增强用户体验.
- 服务迁移对于保持低延迟和用户移动性至关重要.
- 高迁移频率和资源竞争增加了能源消耗和成本.
研究的目的:
- 开发一种在MEC适应性服务迁移的方法,以优化延迟和能源消耗.
- 应对动态环境,资源竞争和MEC服务迁移中的实时决策的挑战.
主要方法:
- 模拟服务迁移作为双目标优化问题 (延迟和能耗).
- 制定了一个混合整数非线性编程问题.
- 设计了一个基于深度Q网络 (DQN) 的模型,NPER-D3QN,集成D3QN,Noisy Net,并为适应性迁移策略优先重复体验.
主要成果:
- 该NPER-D3QN模型生成适应性迁移策略,以实现低延迟和能源消耗.
- 与现有方法相比,模拟实验在延迟和能源效率方面显示出更高的性能.
- 在动态适应和多目标协作优化方面展示了优势.
结论:
- 拟议的延迟和能源意识的适应性服务迁移方法有效地解决了MEC的挑战.
- 在动态和资源竞争激烈的MEC环境中,NPER-D3QN为优化服务迁移提供了一个强大的解决方案.
- 该方法为MEC提供商提供了服务延迟和能源效率的显著改进.
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