通过任务卸载和服务缓存在边缘网络中的线下和在线服务的综合服务质量
Chuangqiang Zhan1, Shaojie Zheng1, Jingyu Chen1
1School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China.
Sensors (Basel, Switzerland)
|July 27, 2024
概括
这项研究优化了边缘服务器对线上和线下数字双胞胎 (DT) 服务的资源配置. 使用深度Q网络 (DQN) 的新算法通过平衡任务卸载和缓存策略来提高服务质量 (QoS).
科学领域:
- 边缘计算是一种边缘计算.
- 分布式系统 分布式系统
- 人工智能的人工智能是人工智能.
背景情况:
- 边缘服务器管理在线和离线数字双胞胎 (DT) 服务,影响资源可用性.
- 现有的研究往往忽视了离线DT服务的资源需求,阻碍了在线任务处理效率.
研究的目的:
- 调查在线和离线边缘服务的综合服务质量 (QoS).
- 为服务缓存和任务卸载制定优化策略,考虑到资源限制.
主要方法:
- 制定了一个效用最大化问题,以平衡在线服务吞吐量和离线服务截止日期.
- 解决了NP-hard问题,将其重新定义为马尔科夫决策过程 (MDP).
- 引入了一个利用深度Q网络 (DQN) 的联合优化算法.
主要成果:
- 拟议的算法有效地考虑了在线和离线服务的资源使用情况.
- 实验结果显示,整体QoS实用性的显著提升.
- 与基线方法相比,该算法至少提高了14.01%的效用.
结论:
- 服务缓存和任务卸载的联合优化对于高效的边缘计算至关重要.
- 基于DQN的方法为边缘环境中复杂的资源分配问题提供了有效的解决方案.
- 这项工作提供了一种实际的方法来提高边缘服务器的性能,以托管各种数字双胞胎服务.
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