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.

Abstract-Content Centric Networking (CCN) is a promising architecture for the diffusion of popular content over the Internet. Radically departing from content-oblivious IP networks, CCN pushes content-awareness down the networking stack. By relying on in-network caching, CCN reduces the overall network load, as requests no longer need to travel until the content originator, but are typically served by a closer CCN router along the path.While the system design of CCN is sound, gathering a reliable estimate of CCN caching performance in the current Internet scenario is challenging, due to its large scale and to the lack of agreement in some critical elements of the evaluation setup. In this work, we add a number of important pieces to the CCN puzzle. First, we pay special attention to the locality of the user request process, as it may be determined by user interest or language barrier. Second, we consider the existence of possibly multiple repositories for the same content, as in the current Internet, along with different CCN interest forwarding policies, exploiting either a single or multiple repositories in parallel.To widen the relevance of our findings, we considering multiple topologies, content popularity settings, caching replacement policies and CCN forwarding strategies. Summarizing our main result, we find that though the use of multiple content repositories can be beneficial from the user point of view, it may however counter part of the benefits in case the CCN strategy layer implements simple forwarding policies.

Most Information Centric Networking designs propose the usage of widely distributed in-network storage. However, the huge amount of content exchanged in the Internet, and the volatility of content replicas cached across the network pose significant challenges to the definition of a scalable routing protocol able to address all available copies. In addition, the number of available copies of a given content item and their distribution among caches is clearly impacted by the request forwarding policy.In this paper we gather initial design considerations for an ICN request forwarding strategy by spanning over two extremes: a deterministic exploitation of forwarding information towards a "known" copy and a random network exploration towards an "unknown" copy, via request flooding. By means of packet-level simulations, we investigate the performance trade-offs of exploitation/exploration approaches, and introduce an hybrid solution. Our forwarding scheme shows a good potential, whether carefully tuned, in terms of delivery performance, implicit cache coordination and possible reduction of forwarding table size.

No abstract

Abstract-Research interest about Information Centric Networking (ICN) has grown at a very fast pace over the last few years, especially after the 2009 seminal paper of Van Jacobson et al. describing a Content Centric Network (CCN) architecture. While significant research effort has been produced in terms of architectures, algorithms, and models, the scientific community currently lacks common tools and scenarios to allow a fair crosscomparison among the different proposals.The situation is particularly complex as the commonly used general-purpose simulators cannot cope with the expected system scale: thus, many proposals are currently evaluated over small and unrealistic scale, especially in terms of dominant factors like catalog and cache sizes. As such, there is need of a scalable tool under which different algorithms can be tested and compared.Over the last years, we have developed and optimized ccnSim, an highly scalable chunk-level simulator especially suitable for the analysis of caching performance of CCN network. In this paper, we briefly describe the tool, and present an extensive benchmark of its performance. To give an idea of ccnSim scalability, a common off-the-shelf PC equipped with 8GB of RAM memory is able to simulate 2-hours of a 50-nodes CCN network, where each nodes is equipped with 10 GB caches, serving a 1 PB catalog in about 20 min CPU time.