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首页> 外文期刊>International journal of ad hoc and ubiquitous computing >Popularity prediction caching based on logistic regression in vehicular content centric networks
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Popularity prediction caching based on logistic regression in vehicular content centric networks

机译：基于车辆内容中心网络逻辑回归的人气预测缓存

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摘要

To improve the network performance caused by mobility and sporadic connectivity in the vehicular network, vehicular content centric network (VCCN) is proposed by applying CCN into the vehicular network. The open in-network caching of CCN makes nodes cache contents cooperatively to facilitate information access. To improve the network performance such as access delay and hit ratio, road side units (RSUs) should try to cache more popular contents and provide better service for mobile users. This paper aims to propose a novel cache replacement policy - popularity prediction content caching (PPCC) for VCCN. In PPCC, we incorporate the future popularity of contents into our decision making. By learning the popularity of contents, we propose a cache replacement method based on logistic regression for RSUs in order to store those frequently access contents. The input data are related to the inherent characters of the received interests and the output is the predicted content popularity which guarantees that only popular contents are cached in the network infrastructures (i.e., RSUs). Simulation evaluations demonstrate that our scheme is very effective with higher cache hit, lower access latency and higher caching efficiency compared to other state-of-the-art schemes.
机译：为了提高由车辆网络中的移动性和散幂连接引起的网络性能，通过将CCN施加到车辆网络中提出了车辆内容中心网络（VCCN）。 CCN的开放式网络缓存使得节点缓存内容协同地，以便于信息访问。为了提高访问延迟和命中率的网络性能，路边单位（RSU）应尝试高速缓存更多流行的内容并为移动用户提供更好的服务。本文旨在提出vccn的新型缓存替换政策 - 普及预测内容缓存（PPCC）。在PPCC中，我们将未来内容的普及纳入我们的决策中。通过学习内容的普及，我们提出了一种基于RSU的Logistic回归的缓存替换方法，以便存储这些频繁访问内容。输入数据与所接收的兴趣的固有字符有关，并且输出是预测的内容流行度，其保证在网络基础架构（即，RSU）中仅缓存流行内容。仿真评估表明，与其他最先进的方案相比，我们的方案非常有效，高速缓存，降低访问等待时间和更高的缓存效率。

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