  As a data-centric cache-enabled architecture, Named Data Networking (NDN) is
considered to be an appropriate alternative to the current host-centric
IP-based Internet infrastructure. Leveraging in-network caching, name-based
routing, and receiver-driven sessions, NDN can greatly enhance the way Internet
resources are being used. A critical issue in NDN is the procedure of cache
allocation and management. Our main contribution in this research is the
analysis of memory requirements to allocate suitable Content-Store size to NDN
routers, with respect to combined impacts of long-term centrality-based metric
and Exponential Weighted Moving Average (EWMA) of short-term parameters such as
users behaviors and outgoing traffic. To determine correlations in such large
data sets, data mining methods can prove valuable to researchers. In this
paper, we apply a data-fusion approach, namely Principal Component Analysis
(PCA), to discover relations from short- and long-term parameters of the
router. The output of PCA, exploited to mine out raw data sets, is used to
allocate a proper cache size to the router. Evaluation results show an increase
in the hit ratio of Content-Stores in sources, and NDN routers. Moreover, for
the proposed cache size allocation scheme, the number of unsatisfied and
pending Interests in NDN routers is smaller than the Degree-Centrality cache
size scheme.
