A content can be replicated in more than one node, in Information Centric Networks (ICNs). Thus, more than one path can be followed to reach the same content, and it is necessary to decide the interface(s) to be selected in every network node to forward content requests towards such multiple content containers. A multipath forwarding strategy defines how to perform this choice. In this paper we propose a general analytical model to evaluate the effect of multipath forwarding strategies on the performance of an ICN content delivery, whose congestion control follows a receiver driven, loss-based AIMD scheme. We use the model to understand the behavior of ICN multipath forwarding strategies proposed in the literature so far, and to devise and evaluate a novel strategy. The considered multipath forwarding strategies are also evaluated in a realistic network setting, by using the PlanetLab testbed.
* Content Centric Network - CCN A CCN addresses contents by using unique hierarchical names @cite_9 (e.g. foo.com doc1). Big contents are split into chunks, uniquely addressed by names that include the content name and the chunk number (e.g. foo.com doc1 @math CN1). At the reception of the related Data, the AIMD algorithm sets cwnd=2 and the receiver sends out two Interests for the next two chunks.
@cite_9
2026645245 2019085553 1994523529 1995965669
Content Centric Networking (CCN) is a recently proposed information-centric Internet architecture in which the main network abstraction is represented by location-agnostic content identifiers instead of node identifiers. In CCN each content object is divided into packet-size chunks. When a content object is transferred, routers on the path can cache single chunks which they can use to serve subsequent requests from other users. Since content chunks in CCN may be retrieved from a number of different nodes caches, implicit-feedback transport protocols will not be able to work efficiently, because it is not possible to set an appropriate timeout value based on RTT estimations given that the data source may change frequently during a flow. In order to address this problem, we propose in this paper a scalable, implicit-feedback congestion control protocol, capable of coping with RTT unpredictability using a novel anticipated interests mechanism to predict the location of chunks before they are actually served. Our evaluation shows that our protocol outperforms similar receiver-driven protocols, in particular when content chunks are scattered across network paths due to reduced cache sizes, long-tail content popularity distribution or the adoption of specific caching policies. Content Centric Networking (CCN) is an emerging network architecture, shifting from an end-to-end connection to a content centric communication model. Each router in CCN has a content store module to cache the chunks passed by, and is arranged in an arbitrary network topology. It is important to allocate an appropriate cache size to each router in order to both improve the network performance and reduce the economic investment. Previous works have proposed several heterogeneous cache allocation schemes, but the gain brought by these schemes is not obvious. In this paper, we introduce a data mining method into the cache size allocation. The proposed algorithm uses manifold learning to analyze the regularity of network traffic and user behaviors, and classify routers based on their roles in the content delivery. Guided by the manifold learning embedding results, a novel cache size optimization scheme is developed. Extensive experiments have been performed to evaluate the proposed scheme. Simulation results show that the proposed scheme outperforms the existing cache allocation schemes in CCN. Data communication across the Internet has significantly changed under the pressure of massive content delivery. Content-Centric Networking (CCN) rethinks Internet communication paradigm around named data retrieval, in contrast with the host-to-host transport model of TCP IP. Content retrieval is natively pull-based driven by user requests, point-to-multipoint and intrinsically coupled with the availability of network storage. By leveraging the key features of CCN transport, in this paper we propose for the first time a congestion control mechanism realizing efficient multipath communication over content-centric networks. Our proposal is based on a Remote Adaptive Active Queue Management (RAAQM) at the receiver that performs a per-route control of bottleneck queues along the paths. We analyze the stability of the proposed solution and assess its performance by means of CCN packet-level simulations under random and optimal route selection. Content-centric networking (CCN) project, a flavor of information-centric networking (ICN), decouples data from its source by shifting the emphasis from hosts and interfaces to information. As a result, content becomes directly accessible and routable within the network. In this data-centric paradigm, techniques for maintaining content confidentiality and privacy typically rely on cryptographic techniques similar to those used in modern digital rights management (DRM) applications, which often require multiple consumer-to-producer (end-to-end) messages to be transmitted to establish identities, acquire licenses, and access encrypted content. In this paper, we present a secure content distribution architecture for CCN that is based on proxy re-encryption. Our design provides strong end-to-end content security and reduces the number of protocol messages required for user authentication and key retrieval. Unlike widely-deployed solutions, our solution is also capable of utilizing the opportunistic in-network caches in CCN. We also experimentally compare two proxy re-encryption schemes that can be used to implement the architecture, and describe the proof of concept application we developed over CCNx.