适应性和智能定制深度Q网络,用于在移动边缘计算环境中的节能任务卸载
1School of Electronics Engineering, Vellore Institute of Technology, Vellore, 632014, Tamil Nadu, India.
Scientific reports
|February 7, 2026
概括
一个新的AI框架,AICDQN,优化了边缘云系统中的任务卸载. 它减少了延迟和任务丢失,同时提高了对延迟敏感物联网应用程序的能源效率.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 分布式系统 分布式系统
背景情况:
- 边缘云计算正在扩大,提高对高效任务卸载的需求.
- 对延迟敏感的物联网 (IoT) 应用程序需要在动态环境中进行智能调度.
研究的目的:
- 引入一种新的强化学习框架,即适应性和智能定制深度Q网络 (AICDQN),用于优先意识的任务安排.
- 为了增强移动边缘计算系统的实时决策.
主要方法:
- 制定了任务卸载作为马尔科夫决策过程 (MDP).
- 集成了一种混合式门式循环单元长期短期存储器 (GRU-LSTM),用于工作负载预测.
- 雇佣了一个动态决斗双深Q网络代理来卸载本地,边缘和云层的决策.
- 使用优先级意识队列系统 (M/M/1,M/M/c,M/M/∞) 建模的计算节点.
- 实施了动态优先级评分功能和能源意识的调度政策.
主要成果:
- AICDQN实现了延迟减少高达33.39%.
- 在能源效率方面表现出57.74%的改善.
- 与现有算法相比,减少了81.25%的任务丢失率.
- 性能优于深度决定性政策梯度 (DDPG),分布式动态任务卸载 (DDTO-DRL),潜在游戏基于卸载算法 (PGOA) 和用户级在线卸载框架 (ULOOF).
结论:
- AICDQN为边缘云任务卸载提供了一个可扩展和适应的解决方案.
- 该框架有效地处理实时,优先级意识和能源受限制的调度.
- 验证了混合GRU-LSTM预测和Dueling双DQN剂的有效性.
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