基于序列预测的车辆内容网络主动缓存

Qiao Wang, D. Grace
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引用次数: 5

摘要

主动缓存是一种很有前途的方法,可以实现高效的内容交付、减少内容检索延迟并改善车载内容网络中的用户体验。本文提出了一种基于机动性预测的主动缓存方案,利用序列预测算法,即基于序列预测的主动缓存,预测车辆路径上下一个可能的RSU,并对相关内容进行预定位。在拉斯维加斯和曼彻斯特两个地区对四个系统的性能进行了评估。在拉斯维加斯获得的结果表明,该系统优于基线主动缓存系统、非主动缓存系统和无缓存系统。在缓存性能方面,SPPC比非主动缓存系统和基线主动缓存系统分别提高了3倍和2倍以上,与非主动缓存系统和无缓存系统相比,SPPC的平均网络延迟分别降低了18%和24%。曼彻斯特性能基准推广了SPPC系统的应用,肯定了SPPC系统的优越性。本文还提供了用数据挖掘技术解决预测问题的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sequence Prediction-based Proactive Caching in Vehicular Content Networks
Proactive caching is a promising approach to achieve efficient content delivery, reduce content retrieval latency, and improve user experience in vehicular content networks. This paper proposes a mobility prediction based proactive caching scheme utilizing a sequence prediction algorithm, namely Sequence Prediction-based Proactive Caching, to predict the next possible RSU along a vehicle’s path and pre-locate relevant content. Four systems’ performance is evaluated in two areas of Las Vegas and Manchester. The obtained results in Las Vegas have shown that the proposed system outperforms the other three systems i.e., Baseline Proactive Caching system, non-proactive caching system and no-caching system. It is shown to be up to over three times and twice better than the non-proactive caching system and Baseline Proactive Caching system respectively in terms of cache performance and on average, network delay of SPPC is reduced by 18% and 24% compared with non-proactive caching system and no-caching system respectively. Performance benchmark in Manchester generalized the application of SPPC system and asserted its superiority. The paper also gives insight into solving prediction issues with data mining techniques.
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