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A short survey on Role of Bloom Filters in Named Data Networking

机译:布隆过滤器在命名数据网络中的作用的简短调查

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Nowadays, Named Data Networking (NDN) has gained significant importance since there is a shift in the Internet paradigm from host-centric model to content-centric model. Modern users are more concerned about what the data is and not about from where the data is coming. Since NDN has to handle huge amount of data every second, the data structures used in NDN should be efficient and scalable. Bloom Filter (BF), an important category of Probabilistic Data Structures (PDS) has shown embarking results in terms of space and time tradeoffs. This work focuses on various implementations of BF in the domain of NDN along with their advantages and limitations.
机译:如今,由于Internet范式已从以主机为中心的模型转变为以内容为中心的模型,命名数据网络(NDN)变得越来越重要。现代用户更加关注数据是什么,而不是数据来自何处。由于NDN必须每秒处理大量数据,因此NDN中使用的数据结构应高效且可扩展。 Bloom Filter(BF)是概率数据结构(PDS)的重要类别,它在空间和时间权衡方面表现出了出色的成果。这项工作着眼于NDN领域中BF的各种实现方式,以及它们的优点和局限性。

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