Content Placement for Reducing FIB Size in NDN Using GA

In Named-Data-Networking (NDN), we request data by name, not IP address. At that time, request packets are sent by referring to FIB (forwarding information base) of routers. On the other hand, CDN (content delivery network) is widely used as a technology to improve the delivery quality of users in the network. We proposed reducing the FIB entry size by using CDN in the method that allocates the original content to the CDN cache server in the network from the server that publishes its content. However, the content placement on the network affects the performance of the network; therefore, it is difficult to determine the optimal placement when considering not only the FIB size but also other metrics. In this paper, we propose a method to use a genetic algorithm (GA) to not only aggregate FIB entries, but also reduce network load and improve other network metrics. Through the numerical evaluation, we show that the proposed method can reduce the FIB entry size while suppressing the load concentration of network links.

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Publication Details

Published
2025-01-15
DOI
https://doi.org/10.1109/icoin63865.2025.10993003
Primary Topic
Caching and Content Delivery
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article
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article

Content Placement for Reducing FIB Size in NDN Using GA

Hiroki Hashimoto, Noriaki Kamiyama
Caching and Content Delivery
article

Content Placement for Reducing FIB Size in NDN Using GA

Hiroki Hashimoto, Noriaki Kamiyama
article en

Abstract

In Named-Data-Networking (NDN), we request data by name, not IP address. At that time, request packets are sent by referring to FIB (forwarding information base) of routers. On the other hand, CDN (content delivery network) is widely used as a technology to improve the delivery quality of users in the network. We proposed reducing the FIB entry size by using CDN in the method that allocates the original content to the CDN cache server in the network from the server that publishes its content. However, the content placement on the network affects the performance of the network; therefore, it is difficult to determine the optimal placement when considering not only the FIB size but also other metrics. In this paper, we propose a method to use a genetic algorithm (GA) to not only aggregate FIB entries, but also reduce network load and improve other network metrics. Through the numerical evaluation, we show that the proposed method can reduce the FIB entry size while suppressing the load concentration of network links.

Ritsumeikan University (JP)
Openalex Percentile: Top 90%
Caching and Content Delivery
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Content Placement for Reducing FIB Size in NDN Using GA — Hiroki Hashimoto, Noriaki Kamiyama · (2025) | TGRS Research Map | TGRS