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On the effectiveness of the PIT in reducing upstream demand in an NDN router

机译:关于PIT减少NDN路由器上游需求的有效性

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摘要

The paper revisits the performance evaluation of caching in a Named Data Networking (NDN) router where the content store (CS) is supplemented by a pending interest table (PIT). The PIT aggregates requests for a given content that arrive within the download delay and thus brings an additional reduction in upstream bandwidth usage beyond that due to CS hits. We extend prior work on caching with non-zero download delay (non-ZDD) by proposing a novel mathematical framework that is more easily applicable to general traffic models and by considering alternative cache insertion policies. Specifically we evaluate the use of an LRU filter to improve CS hit rate performance in this non-ZDD context. We also consider the impact of time locality in demand due to finite content lifetimes. The models are used to quantify the impact of the PIT on upstream bandwidth reduction, demonstrating notably that this is significant only for relatively small content catalogues or high average request rate per content. We further explore how the effectiveness of the filter with finite content lifetimes depends on catalogue size and traffic intensity. (C) 2020 Elsevier B.V. All rights reserved.
机译:本文回顾了命名数据网络(NDN)路由器中缓存的性能评估,其中内容存储(CS)由未决兴趣表(PIT)补充。 PIT汇总了在下载延迟之内到达的对给定内容的请求,因此,除了CS命中之外,还带来了上游带宽使用量的进一步减少。我们通过提出一种更容易适用于一般流量模型的新颖数学框架并考虑替代缓存插入策略,来扩展具有非零下载延迟(non-ZDD)的缓存的现有工作。具体来说,我们评估了在这种非ZDD上下文中使用LRU滤波器来改善CS命中率性能的情况。我们还考虑了由于内容寿命有限而造成的时间本地需求的影响。这些模型用于量化PIT对上游带宽减少的影响,特别表明,这仅对于相对较小的内容目录或较高的每内容平均请求率才有意义。我们将进一步探讨内容寿命有限的过滤器的有效性如何取决于目录大小和流量强度。 (C)2020 Elsevier B.V.保留所有权利。

著录项

  • 来源
    《Performance Evaluation》 |2020年第4期|102081.1-102081.15|共15页
  • 作者

  • 作者单位

    Sharif Univ Technol Tehran Iran;

    Telecom ParisTech Paris France;

    Politecn Torino Turin Italy;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Named data networking; Caching; Request aggregation; Content popularity;

    机译:命名数据网络;缓存;请求聚合;内容受欢迎;

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