Randomized SVD Based Probabilistic Caching Strategy in Named Data Networks

Abstract

Named Data Networking (NDN), a data-centric enabled-cache architecture, as one of the candidates for the future Internet, has the potential to overcome many of the current Internet difficulties (emph{e.g.}, security, mobility, multicasting). Influenced by using cache in intermediate equipment, NDN has gained attention as a prominent method of Internet content sharing. Managing the NDN caches and reducing the cache redundancy are the important goals in this paper. Our main contribution in this research is toward caching optimization in comparison with betweenness probabilistic in-network caching strategy. Therefore, with respect to combined impacts of long-term centrality-based metric and Linear Weighted Moving Average (LWMA) of short-term parameters such as user incoming pending requests and unique outgoing hit requests on caching management, a flexible probability caching strategy is proposed. Moreover, a simple Randomized-SVD approach is applied to combine averaged short-term and long-term metrics. The output of this data-fusion algorithm is used to allocate a proper probability to the caching strategy. Evaluation results display an increase in the hit ratios of NDN routers' content-stores for the proposed method. In addition, the producer's hit ratio and the Interest-Data Round Trip Time, compared to the betweenness scheme, is decreased.