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Popularity-Aware Closeness Based Caching in NDN Edge Networks.

Marica Amadeo1,2, Claudia Campolo1,2, Giuseppe Ruggeri1,2

  • 1DIIES Department, University Mediterranea of Reggio Calabria, 89100 Reggio Calabria, Italy.

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Summary

This study introduces a new caching strategy for Named Data Networking (NDN) edge networks. The popularity-aware closeness (PaC) metric improves content retrieval by caching popular data closer to users, reducing delay and traffic.

Keywords:
6GNamed Data Networkingcachingedge networksinformation centric networking

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Area of Science:

  • Computer Science
  • Networking
  • Edge Computing

Background:

  • Named Data Networking (NDN) offers name-based routing and in-network caching for 6G edge networks.
  • Effective caching strategies are crucial for NDN performance, with prior work highlighting content popularity and network topology.
  • Current distributed caching in NDN nodes faces challenges in optimizing content retrieval efficiency.

Purpose of the Study:

  • To propose a novel distributed caching strategy for NDN edge networks.
  • To introduce the popularity-aware closeness (PaC) metric for optimizing cache placement.
  • To enhance content retrieval performance by strategically caching popular content.

Main Methods:

  • Developed a distributed caching strategy based on the popularity-aware closeness (PaC) metric.
  • PaC measures a node's proximity to the majority of content requesters.
  • Identified popular content and cached it on edge nodes with the highest PaC values.

Main Results:

  • The proposed PaC-based caching strategy significantly outperforms benchmark schemes.
  • Demonstrated reductions in content retrieval delay.
  • Showcased a decrease in exchanged data traffic within the NDN edge network.

Conclusions:

  • The popularity-aware closeness (PaC) strategy is an effective approach for NDN edge network caching.
  • Strategic caching based on requester proximity optimizes network resource utilization.
  • This method enhances overall NDN performance by minimizing latency and data overhead.