Content-centric networking (CCN) is a promising framework to rebuild the Internet's forwarding substrate around the concept of content. CCN advocates ubiquitous in-network caching to enhance content delivery, and thus each router has storage space to cache frequently requested content. In this work, we focus on the cache allocation problem, namely, how to distribute the cache capacity across routers under a constrained total storage budget for the network. We first formulate this problem as a content placement problem and obtain the optimal solution by a two-step method. We then propose a suboptimal heuristic method based on node centrality, which is more practical in dynamic networks with frequent content publishing. We investigate through simulations the factors that affect the optimal cache allocation, and perhaps more importantly we use a real-life Internet topology and video access logs from a large scale Internet video provider to evaluate the performance of various cache allocation methods. We observe that network topology and content popularity are two important factors that affect where exactly should cache capacity be placed. Further, the heuristic method comes with only a very limited performance penalty compared to the optimal allocation. Finally, using our findings, we provide recommendations for network operators on the best deployment of CCN caches capacity over routers.

Content-Centric Networking (CCN) is a promising framework for evolving the current network architecture, advocating ubiquitous in-network caching to enhance content delivery. Consequently, in CCN, each router has storage space to cache frequently requested content. In this work, we focus on the cache allocation problem: namely, how to distribute the cache capacity across routers under a constrained total storage budget for the network. We formulate this problem as a content placement problem and obtain the exact optimal solution by a two-step method. Through simulations, we use this algorithm to investigate the factors that affect the optimal cache allocation in CCN, such as the network topology and the popularity of content. We find that a highly heterogeneous topology tends to put most of the capacity over a few central nodes. On the other hand, heterogeneous content popularity has the opposite effect, by spreading capacity across far more nodes. Using our findings, we make observations on how network operators could best deploy CCN caches capacity.

The scalability of routing architectures for large networks is one of the biggest challenges that the Internet faces today. Greedy routing, in which each node is assigned a locator used as a distance metric, recently received increased attention from researchers and is considered as a potential solution for scalable routing. In this paper, we propose LMD - a Local Minimum Driven method to compute the topology-based locator. As opposed to previous work, our algorithm employs a quasigreedy and self-organized embedding method, which outperforms similar decentralized algorithms by up to 20% in success rate. To eliminate the negative effect of the “quasi” greedy property - transfer routes longer than the shortest routes, we introduce a two-stage routing strategy, which combines the greedy routing with source routing. The greedy routing path discovered and compressed in the first stage is then used by the following source-routing stage. Through extensive evaluations, based on synthetic topologies as well as on a snapshot of the real Internet AS topology, we show that LMD guarantees 100% delivery rate on large networks with a very low stretch.

In this paper we propose FPC - a Force-based layout and Path Compressing routing schema for scale-free network. As opposed to previous work, our algorithm employs a quasi-greedy but self-organized and configuration-free embedding method - force-based layout. In order to eliminate the negative influences of the “quasi” greedy property, we present a two-stage routing strategy, which combines the greedy routing with source routing. The greedy routing path discovered and compressed in a first stage is then used by the following source-routing stage. The detailed evaluation based on synthetic topologies as well as on a real Internet AS topology shows that: FPC guarantees 100% delivery rates on scale-free networks with an attractive low stretch (e.g. less than 1.2 on the real Internet AS topology).