On sizing CCN content stores by exploiting topological information
Résumé
In this work, we study the caching performance of
Content Centric Networking (CCN), with special emphasis on the
size of individual CCN router caches. Specifically, we consider
several graph-related centrality metrics (e.g., betweenness, closeness, stress, graph, eccentricity and degree centralities) to allocate
content store space heterogeneously across the CCN network, and
contrast the performance to that of an homogeneous allocation.
To gather relevant results, we study CCN caching performance
under large cache sizes (individual content stores of 10 GB),
realistic topologies (up to 60 nodes), a YouTube-like Internet
catalog (108 files for 1PB video data). A thorough simulation
campaign allow us to conclude that (i) , the gain brought by
content store size heterogeneity is very limited, and that (ii) the
simplest metric, namely degree centrality, already proves to be
a “sufficiently good” allocation criterion.
On the one hand, this implies rather simple rules of thumb for
the content store sizing (e.g., “if you add a line card to a CCN
router, add some content store space as well”). On the other
hand, we point out that technological constraints, such as line-
speed operation requirement, may however limit the applicability
of degree-based content store allocation.