In this paper, we use the MCA (Multi-Cache Approximation) algorithm to numerically determine cache hit probability in a multi-cache network. We then analytically obtain performance metrics for Content-Centric networking (CCN). Our analytical model contains multiple routers, multiple repositories (e.g., storage servers), and multiple entities (e.g., clients). We obtain three performance metrics: content delivery delay (i.e., the average time required for an entity to retrieve a content through a neighboring router), throughput (i.e., number of contents delivered from an entity per unit of time), and availability (i.e., probability that an entity can successfully retrieve a content from a network). Through several numerical examples, we investigate how network topology affects the performance of CCN. A notable finding is that content caching becomes more beneficial in terms of content delivery time and availability (resp., throughput) as distance between the entity and the requesting repository narrows (resp., widens).

In this paper, we focus on a large-scale ICN (Information-Centric Networking), and reveal the scaling property of ICN. Because of in-network content caching, ICN is a sort of cache networks and expected to be a promising architecture for replacing future Internet. To realize a global-scale (e.g., Internet-scale) ICN, it is crucial to understand the fundamental properties of such large-scale cache networks. However, the scaling property of ICN has not been well understood due to the lack of theoretical foundations and analysis methodologies. For answering research questions regarding the scaling property of ICN, we derive the cache hit probability at each router, the average content delivery delay of each entity, and the average content delivery delay of all entities over a content distribution tree comprised of a single repository (i.e., content provider), multiple routers, and multiple entities (i.e., content consumers). Through several numerical examples, we investigate the effect of the topology and the size of the content distribution tree and the cache size at routers on the average content delivery delay of all entities. Our findings include that the average content delivery delay of ICNs converges to a constant value if the cache size of routers are not small, which implies high scalability of ICNs, and that even when the network size would grow indefinitely, the average content delivery delay is upper-bounded by a constant value if routers in the network are provided with a fair amount of content caches.

In this paper, we propose a packet loss detection mechanism called Interest ACKnowledgement (ACK). Interest ACK provides information on the history of successful Interest packet receptions at a repository (i.e., content provider); this information is conveyed to the corresponding entity (i.e., content consumer) via the header of Data packets. Interest ACKs enable the entity to quickly and accurately detect Interest and Data packet losses in the network. We conduct simulations to investigate the effectiveness of Interest ACKs under several scenarios. Our results show that Interest ACKs are effective for improving the adaptability and stability of CCN with window-based flow control and that packet losses at the repository can be reduced by 10%-20%. Moreover, by extending Interest ACK, we propose a lossy link detection mechanism called LLD-IA (Lossy Link Detection with Interest ACKs), which is a mechanism for an entity to estimate the link where the packet was discarded in a network. Also, we show that LLD-IA can effectively detect links where packets were discarded under moderate packet loss ratios through simulation.