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Local Tree Hunting: Finding Closest Contents from In-Network Cache

机译:本地树搜寻:从网络内缓存中查找最近的内容

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How to retrieve the closest content from an in-network cache is one of the most important issues in Information-Centric Networking (ICN). This paper proposes a novel content discovery scheme called Local Tree Hunting (LTH). By adding branch-cast functionality to a local tree for content requests to a Content-Centric Network (CCN) response node, the discovery area for caching nodes expands. Since the location of such a branch-casting node moves closer to the request node when the content is more widely cached, the discovery range, i.e. the branch size of the local tree, becomes smaller. Thus, the discovery area is autonomously adjusted depending on the content dissemination. With this feature, LTH is able to find the “almost true closest” caching node without checking all the caching nodes in the in-network cache. The performance analysis employed in Zipf's law content distribution model and which uses the Least Recently Used eviction rule shows the superiority of LTH with respect to identifying the almost exact closest cache.
机译:如何从网络内缓存中检索最接近的内容是信息中心网络(ICN)中最重要的问题之一。本文提出了一种新颖的内容发现方案,称为本地树搜寻(LTH)。通过向本地树添加分支广播功能以向内容中心网络(CCN)响应节点发送内容请求,用于缓存节点的发现区域得以扩展。由于当内容被更广泛地高速缓存时,这种分支广播节点的位置移近请求节点,因此发现范围,即本地树的分支大小变小。因此,根据内容传播自主地调整发现区域。借助此功能,LTH能够找到“几乎是最接近的”缓存节点,而无需检查网络内缓存中的所有缓存节点。 Zipf的法律内容分配模型中使用的性能分析(使用最近最少使用的逐出规则)显示了LTH在识别几乎精确的最近缓存方面的优越性。

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