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Interest Relevance-Based Caching Design in Content-Centric Networking

  • Guozhi Zhang
  • Jiqiang Liu
  • Xiaolin Chang
  • Yang Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11336)

Abstract

Among the existing Content Center Networking (CCN) caching schemes, the most important category is popularity-based schemes which perform better than non-popularity-based in terms of cache-hits. However, these existing popularity-based caching schemes assumed that they provide services for a single type of applications and assumed that content requests (interest) conform to Zipf-like distribution. Although Zipf-like request distribution was validated in many network applications, this distribution may not exist at the node level in CCN when there exist multiple types of upper-level applications in the network. Once the traffic feature of Zipf-like distribution becomes less obvious, the existing popularity-based caching schemes could not work well. Therefore, how to predict the content request (interest) for each node becomes a key problem of caching design.

In this paper, we use the application-level relevance of interest to assist the caching design, rather than just relying on names. We propose a scheme (named as ICDCS) based on interest/content tag analyzing in which multiple types of upper-level applications produced contents/interests and tags is added as a part of name, then a measuring mechanism is designed to count and predict the trend of interest. Our scheme can be well combined with existed approaches and improve their caching performance. Simulations over various system parameters are done to validate the effectiveness and efficiency of ICDCS.

Keywords

Tagging system Content centric networking Cache allocation strategy Naming design 

Notes

Acknowledgements

This work was supported in part by the Natural Science Foundation of China under Grants 61672092 and 61572066, and in part by the Fundamental Research Funds for the Central Universities of China under Grants 2018JBZ103.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Beijing Key Laboratory of Security and Privacy in Intelligent TransportationBeijing Jiaotong UniversityBeijingChina
  2. 2.The School of Computer and EngineeringNorthwest Normal UniversityLanzhouChina

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