Integrated PoolCache and LRFU Caching Strategy for Efficient Content Distribution in Named Data Networking

Abstract

The rapid growth of internet users has significantly increased data traffic, demanding efficient content distribution architectures. Named Data Networking (NDN), an implementation of Information-Centric Networking (ICN), addresses this through in-network caching at router nodes. However, existing NDN caching strategies tend to address either cache placement or cache replacement in isolation. Placement strategies such as PoolCache effectively eliminate intra-cluster content redundancy through cooperative caching, but rely on standard Least Recently Used (LRU) replacement. Replacement strategies such as Least Recently and Frequently Used (LRFU) improve cache utilization through Combined Recency-Frequency (CRF) scoring, but without collaborative placement coordination. This paper proposes an integrated caching strategy that combines PoolCache and LRFU to simultaneously address both challenges. PoolCache manages caches across neighboring nodes as a shared resource, assigning each content to exactly one node per cluster via consistent hashing without extensive inter-node communication. LRFU complements this by evicting content with the lowest CRF score, retaining content that is both recently and frequently accessed. The integration is implemented and evaluated in ndnSIM 2.7 under two scenarios: variation of Content Store (CS) deployment percentage (5%-100%) and consumer demand rate (5, 20, 50, and 100 pkt/s) with Zipf-Mandelbrot content distribution $(\alpha=0.7)$. Results show that PoolCache+LRFU consistently outperforms PoolCache+LRU across all evaluated conditions. Under the CS percentage scenario, PoolCache+LRFU achieves a hit ratio up to 0.3666 compared to 0.3574 for PoolCache+LRU at $\mathbf{C S} \boldsymbol{=} \mathbf{1 0 0} \boldsymbol{\%}$, with the largest gap at $\mathbf{C S} \boldsymbol{=} \mathbf{2 0} \boldsymbol{\%}(\mathbf{0. 1 7 4 5}$ vs. 0.1375). Under varying demand, PoolCache+LRFU maintains stable average retrieval latency of 0.0318 s at 100 pkt/s, whereas PoolCache+LRU saw a performance decline to 0.0470 seconds, representing a 32.3% increase. Content redundancy is effectively suppressed by PoolCache in both configurations.