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Two-stage Update Strategy for Edge Caching Deployment Based on Access Information in Ultra Dense Network

机译:超密集网络中基于访问信息的边缘缓存部署两阶段更新策略

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The explosion of data traffic has an unfavorable effect on communication latency and the overhead of content access in Ultra Dense Network (UDN). Content-based edge caching is deemed to be an efficient solution. In this paper, we present a caching structure and propose a two-stage update strategy in order to reduce the file-access latency and cost in an application scenario covered by the dense base-stations and Wi-Fi signals where users are stationary or moving at a low speed. In the UDN architecture, the proposed strategy can obtain access information based on the combination of user preferences and file attributes to cache high-popularity files on the Radio Access Network (RAN) side. We also apply a transfer actor-critic learning framework to save energy consumption of the base station groups. In addition, filtering of junk files is taken into account to increase the utilization of cache storage space. Finally, we assess the proposed scheme by plenty of simulations. The results show significant reduction on file-access latency and cost as a result of the two-stage update strategy for edge caching deployment method.
机译:数据流量的爆炸性增长对通信延迟和超密集网络(UDN)中内容访问的开销产生了不利影响。基于内容的边缘缓存被认为是一种有效的解决方案。在本文中,我们提出了一种缓存结构,并提出了一种两阶段更新策略,以减少在用户静止或移动的密集基站和Wi-Fi信号所覆盖的应用场景中的文件访问延迟和成本。低速行驶。在UDN体系结构中,提出的策略可以基于用户首选项和文件属性的组合获得访问信息,以在无线访问网络(RAN)端缓存高人气文件。我们还应用了转移参与者批判性学习框架,以节省基站组的能耗。另外,还考虑了对垃圾文件的过滤以提高缓存存储空间的利用率。最后,我们通过大量仿真评估了提出的方案。结果表明,由于采用了边缘缓存部署方法的两阶段更新策略,因此文件访问延迟和成本显着降低。

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