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https://dspace.iiti.ac.in/handle/123456789/16229
Title: | EeNCache : Neighborhood Cooperative Content Caching and Delivery in Named Data Networks |
Authors: | Chaudhary, Pankaj Hubballi, Neminath |
Keywords: | Bloom Filter;Caching;Content Delivery;Named Data Networks;Next-generation Networks |
Issue Date: | 2025 |
Publisher: | Institute of Electrical and Electronics Engineers Inc. |
Citation: | Chaudhary, P., & Hubballi, N. (2025). EeNCache : Neighborhood Cooperative Content Caching and Delivery in Named Data Networks. 2025 International Conference on Computing, Networking and Communications, ICNC 2025. https://doi.org/10.1109/ICNC64010.2025.10993651 |
Abstract: | Named Data Networking (NDN) offers in-network caching which helps to improve content delivery efficiency and minimizes bandwidth usage. However, cache being a prized commodity needs to be used judiciously. Hence, many studies propose different caching techniques aimed at improving content delivery. However, achieving a balance between good performance and overhead in real-world scenarios remains a challenge. In this paper, we outline a lightweight neighborhood cooperative searching and caching technique for NDN, named EeNCache. It cooperates with routers in the neighborhood to base its caching and searching decisions. This cooperation is achieved through routers periodically sharing their cache catalogue with their neighbors. This catalogue is maintained in the form of a Bloom filter data structure. In addition, EeNCache also uses a simple mechanism to cache popular content. The performance of EeNCache is evaluated on a large-scale network topology, comparing it with traditional and recent caching techniques using the Python-based Icarus simulator. Our findings demonstrate that cooperative caching yields better results in terms of cache hit ratio and content access time. © 2025 IEEE. |
URI: | https://dx.doi.org/10.1109/ICNC64010.2025.10993651 https://dspace.iiti.ac.in:8080/jspui/handle/123456789/16229 |
Type of Material: | Conference Paper |
Appears in Collections: | Department of Computer Science and Engineering |
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