CPePC: Cooperative and Predictive Popularity based Caching for Named Data Networks
Pankaj Chaudhary, Neminath Hubballi, and Sameer G. Kulkarni

TL;DR
CPePC introduces a cooperative, predictive caching scheme for Named Data Networks that reduces overhead and improves cache efficiency by community-based popularity estimation and forecasting caching parameters.
Contribution
It proposes a novel cooperative caching method that minimizes popularity estimation overhead and incorporates predictive caching decisions based on community detection.
Findings
Outperforms six state-of-the-art caching techniques in simulations
Reduces overhead in popularity estimation
Improves cache hit ratio and network efficiency
Abstract
Caching content is an inherent feature of Named Data Networks. Limited cache capacity of routers warrants that the choice of content being cached is judiciously done. Existing techniques resort to caching popular content to maximize utilization. However, these methods experience significant overhead for coordinating and estimating the popularity of content. To address this issue, in this paper, we present CPePC, which is a cooperative caching technique designed to improve performance. It accomplishes this through a combination of two factors. First, CPePC enhances efficiency by minimizing the overhead of popularity estimation. Second, it forecasts a parameter that governs caching decisions. Efficiency in popularity estimation is achieved by dividing the network into several non-overlapping communities using a community estimation algorithm and selecting a leader node to coordinate this…
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Taxonomy
TopicsCaching and Content Delivery · Advanced Data Storage Technologies · Ammonia Synthesis and Nitrogen Reduction
