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通过内容受欢迎程度和人群预测进行动态边缘缓存,用于短视频服务.
Sen Niu1, Yuhe Liu1, Kaili Liao2
1School of Computer and Information Engineering, Institute for Artificial Intelligence, Shanghai Polytechnic University, Shanghai, 201209, China.
Scientific reports
|November 26, 2025
概括
通过内容受欢迎性和人群预测 (DECC) 实现动态边缘缓存,改善了短视频的移动网络缓存. 这种AI框架通过预测内容受欢迎程度和用户行为来提高缓存命中率并减少延迟.
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:
- 短视频流量的增长需要有效的移动网络缓存.
- 传统的缓存方法在动态,个性化的短视频内容上扎,因为它们依赖于静态的受欢迎度量.
研究的目的:
- 提出DECC (Dynamic Edge-caching through Content Popularity and Crowd Prediction),这是一个用于优化移动网络边缘缓存的新型框架.
- 通过联合建模内容受欢迎程度和用户访问行为来解决传统缓存的局限性.
主要方法:
- DECC使用混合深度学习架构 (Conv1D,LSTM,GRU) 来分析视频请求和用户活动的时间动态.
- 融合机制通过可适应内容放置的双路径预测生成缓存优先级分数.
主要成果:
- 与基线方法相比,DECC显著提高了缓存的命中率.
- 该框架有效地减少了访问延迟,并提高了整体资源利用效率.
- 在真实世界数据集上的实验评估验证了DECC的性能.
结论:
- DECC为下一代短视频服务中的边缘缓存提供了一个可扩展和智能化解决方案.
- 内容受欢迎程度和人群预测的联合建模优化了动态内容的缓存决策.
- DECC在移动网络效率的关键指标上表现出卓越的表现.
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