Coordinated En-Route Web Caching
IEEE Transactions on Computers
Incremental Nonlinear Dimensionality Reduction by Manifold Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive content management in structured P2P communities
InfoScale '06 Proceedings of the 1st international conference on Scalable information systems
On the optimization of storage capacity allocation for content distribution
Computer Networks: The International Journal of Computer and Telecommunications Networking
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
On content-centric router design and implications
Proceedings of the Re-Architecting the Internet Workshop
Modelling and evaluation of CCN-caching trees
NETWORKING'11 Proceedings of the 10th international IFIP TC 6 conference on Networking - Volume Part I
Bandwidth and storage sharing performance in information centric networking
Proceedings of the ACM SIGCOMM workshop on Information-centric networking
A reality check for content centric networking
Proceedings of the ACM SIGCOMM workshop on Information-centric networking
Modeling data transfer in content-centric networking
Proceedings of the 23rd International Teletraffic Congress
Optimal Web cache sizing: scalable methods for exact solutions
Computer Communications
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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.