On sizing CCN content stores by exploiting topological information

Abstract

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 (10 8 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 linespeed operation requirement, may however limit the applicability of degree-based content store allocation.

Coordinated Selfish Distributed Caching for Peering Content-Centric Networks

“…General topologies were considered for content-centric networks [4], [13], [6]. An iterative algorithm Scale up of arrival intensity for ISP 1 Relative permanence in ISP 1 BA node degree=5 BA node degree=15 1st ranked item 2nd ranked item 3rd ranked item to approximate the cache miss rate in a network of caches was proposed in [13].…”

“…An iterative algorithm Scale up of arrival intensity for ISP 1 Relative permanence in ISP 1 BA node degree=5 BA node degree=15 1st ranked item 2nd ranked item 3rd ranked item to approximate the cache miss rate in a network of caches was proposed in [13]. The authors in [4] considered various network topology-aware policies to improve the overall cache hit rate in a network of caches. In [6], [7] the authors use probabilistic caching to increase the cache hit rate and the fairness among content flows in a network of caches.…”

“…The resulting network is often modeled as a network of interacting caches. Several recent works aimed at optimizing the performance of a cache network through dimensioning cache sizes as a function of their location in the cache network [4], by routing interest messages to efficiently find contents [5] and by tuning the cache eviction policies used by the individual caches [6], [7].…”

Neighborhood-Based In-Network Caching for Information-Centric Networks

“…All routers in a path have the same cache content in the LCE (Leave Copy Everywhere) scheme [4], which incurs high content redundancy. Part of routers along a path can be properly selected according to a probability model [5] or graphbased metrics such as node degree and betweenness [6] [7]. In off-path caching, data contents are cached in a certain set of routers in the network and their locations are determined by a hash function [8].…”

ccnSim: An highly scalable CCN simulator

“…While our previous research has focused on the performance of CCN gathered through ccnSim [14]- [16], [21], this paper focuses on the performance of the ccnSim tool itself. Specifically, two crucial aspects have been taken into account in the design of ccnSim, namely a) Memory occupancy: The simulation of very large catalogs and very large cache size is essential to gather CCN performance under realistic settings.…”