As a data-centric cache-enabled architecture, Named Data Networking (NDN) is considered to be an appropriate alternative to the current host-centric IP-based Internet infrastructure. Leveraging in- network caching, name-based routing, and receiver-driven sessions, NDN can greatly enhance the way Internet resources are being used. A critical issue in NDN is the procedure of cache allocation and management. Our main contribution in this research is the analysis of memory requirements to allocate suitable Content-Store size to NDN routers, with respect to combined impacts of long-term centrality-based metric and Exponential Weighted Moving Average (EWMA) of short-term parameters such as users behaviours and outgoing traffic. To determine correlations in such large data sets, data mining methods can prove valuable to researchers. In this paper, we apply a data-fusion approach, namely Principal Component Analysis (PCA), to discover relations from short- and long-term parameters of the router. The output of PCA, exploited to mine out raw data sets, is used to allocate a proper cache size to the router. Evaluation results show an increase in the hit ratio of Content-Stores in sources, and NDN routers. Moreover, for the proposed cache size allocation scheme, the number of unsatisfied and pending Interests in NDN routers is smaller than the Degree-Centrality cache size scheme.
@cite_9: Abstract Information Centric Network (ICN) is an emerging network paradigm centered around the named contents rather than the host-to-host connectivity. The common characteristic of ICN leverages in-network caching to achieve an efficient and reliable content distribution but also brings challenges. The in-network caching technique equips all ICN routers with cache storage. However, no existing works focus on the cache storage sharing mechanism among different applications to satisfy the line speed requirements and diversity of applications in ICN. In this paper, we formulate the per-application storage management problem into an optimized resource allocation problem and introduce a manifold learning method to classify the priority of applications. Dynamic programming is adopted to solve the formulated problem and an adaptive per-application storage management scheme is proposed on the basis of the optimal solutions. Extensive experiments have been performed to evaluate the proposed scheme and show that our approach is superior to static partitioning and shared storage schemes.
@cite_17: 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.
Authors in @cite_17 have introduced a novel cache size optimization scheme and classify cache routers based on their roles in content delivery. The information about users' behavior and network traffic distribution are collected by the number of received requests, requests served and content replacements. Their scheme has showed better performance than the graph-related schemes as the important routers have been allocated 80 In @cite_9 , a scheme has been presented to give higher cache resource to applications with high performance requirement. In order to assign priority to applications, a manifold learning method has been introduced to perform data mining. The presented evaluations show that the hit ratio of applications such as HTTP web and file sharing is more than Web media and User Generated Contents (such as blogs, chats, tweets). As claimed, the differences are from popularity and average content size of the applications.