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A Filter-Based Design of Pending Interest Table in Named Data Networking

机译:命名数据网络中基于过滤器的未决权益表设计

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摘要

Named data networking (NDN) is a recently proposed networking model that intends to overcome the restrictions of the current Internet Protocol (IP). Many research works have been attracted to this modern Internet architecture but the pending interest table (PIT), which is one of the most important constituent of NDN node, did not take considerable concern. The PIT plays an essential role in the communication process for NDN nodes and in both directions (i.e. receiving interest packets during upstream way and receiving data packets during downstream way). PIT should be sufficiently large to accommodate a large amount of information. Also, It must be fast adequate to not be a bottleneck in the forwarding process. In this paper, we propose a novel PIT implementation called FTDF-PIT for NDN. This new design depends on employing an approximate data structure that called fast two dimensional filter (FTDF) which has better performance than the other proposed filters (Bloom and Quotient filters). This filter has high performance in terms of: insertion/deletion/query throughput, low memory requirements, and low false positive rate which makes it very suitable for implementing PIT. Our evaluations show that utilizing FTDF-PIT can result in many advantages over the existing solutions in terms of: reducing the memory consumption, minimizing false positive rate, and increasing the building and updating processes which make it very suitable to meet the current network demands.
机译:命名数据网络(NDN)是最近提出的一种网络模型,旨在克服当前Internet协议(IP)的限制。许多研究工作已经吸引到这种现代Internet体系结构,但是作为NDN节点最重要组成部分之一的未决利益表(PIT)并没有引起人们的广泛关注。 PIT在NDN节点的通信过程中以及双向通信中都起着至关重要的作用(即,在上游方式中接收兴趣数据包,在下游方式中接收数据数据包)。 PIT应该足够大以容纳大量信息。而且,必须足够快,不要成为转发过程中的瓶颈。在本文中,我们为NDN提出了一种新颖的PIT实现,称为FTDF-PIT。此新设计依赖于采用称为快速二维滤波器(FTDF)的近似数据结构,该结构比其他建议的滤波器(布隆和商数滤波器)具有更好的性能。该过滤器在以下方面具有高性能:插入/删除/查询吞吐量,较低的内存要求和较低的误报率,这使其非常适合实现PIT。我们的评估表明,与以下现有解决方案相比,使用FTDF-PIT可以带来许多优势:减少内存消耗,最小化误报率以及增加构建和更新过程,使其非常适合满足当前网络需求。

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