基于车辆边缘网络中的时间卷积网络的混合合作缓存
Honghai Wu1, Jichong Jin1, Huahong Ma1
1School of Information Engineering, Henan University of Science and Technology, Luoyang 471000, China.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
本研究介绍了用于智能汽车的混合内容价值协作缓存策略 (THCS). 通过考虑内容价值和协作,THCS优化了边缘缓存,提高了缓存命中率并减少了交付延迟.
科学领域:
- 智能运输系统 智能运输系统
- 无线网络无线网络.
- 网络缓存 网络缓存
背景情况:
- 对智能汽车服务的需求不断增加,推动了大量的无线网络流量.
- 当前边缘缓存策略侧重于内容的受欢迎程度,导致冗余和低效.
- 边缘缓存为高效的数据传输提供了位置优势.
研究的目的:
- 为边缘网络提出一种新的混合内容价值协作缓存策略 (THCS).
- 解决当前主流缓存解决方案中的缓存冗余和低效率问题.
- 在智能汽车环境中,优化缓存内容并减少内容交付延迟.
主要方法:
- 利用时间卷积网络 (TCN) 准确地预测内容的受欢迎程度.
- 开发一个混合内容值 (HCV) 度量,考虑缓存内容的多个因素.
- 采用动态编程算法来最大限度地提高整体HCV和优化缓存决策.
主要成果:
- 与基准方案相比,提议的THCS策略显示缓存命中率有12.3%的改善.
- THCS实现了16.7%的内容传输延迟降低.
- 模拟实验验证了协作缓存方法的有效性.
结论:
- THCS 策略通过允许边缘节点之间的协作来有效优化边缘缓存.
- 考虑到混合内容的价值以及受欢迎程度,可以提高缓存效率并减少延迟.
- 这种方法为在不断变化的智能汽车环境中管理网络流量提供了可行的解决方案.
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