PF-ClusterCache: Popularity and Freshness-Aware Collaborative Cache Clustering for Named Data Networking of Things

Abstract

Named Data Networking (NDN) has been recognized as the most promising information-centric networking architecture that fits the application model of IoT systems. In-network caching is one of NDN’s most fundamental features for improving data availability and diversity and reducing the content retrieval delay and network traffic load. Several caching decision algorithms have been proposed; however, retrieving and delivering data content with minimal resource usage, reduced communication overhead, and a short retrieval time remains a great challenge. In this article, we propose an efficient popularity and freshness caching approach named PF-ClusterCache that efficiently aggregates the storage of different nodes within a given cluster as global shareable storage so that zero redundancy be obtained in any cluster of nodes. This increases the storage capacity for caching with no additional storage resource. PF-ClusterCache ensures that only the newest, most frequent data content is cached, and caching is only performed at the edge of the network, resulting in a wide diversity of cached data content across the entire network and much better overall performance. In-depth simulations using the ndnSIM simulator are performed using a large transit stub topology and various networking scenarios. The results show the effectiveness of PF-ClusterCache in sharing and controlling the local global storage, and in accounting for the popularity and freshness of data content. PF-ClusterCache clearly outperforms the benchmark caching schemes considered, especially in terms of the significantly greater server access reduction and much lower content retrieval time, while efficiently conserving network resources.

Keywords

• NDN
• IoT
• in-network caching
• data freshness
• data popularity
• clustering