You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
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.

Cited Abstract(s):
[1]: In this work, we study the caching performance of Content Centric Networking (CCN), with special emphasis on the size of individual CCN router caches. Specifically, we consider several graph-related centrality metrics (e.g., betweenness, closeness, stress, graph, eccentricity and degree centralities) to allocate content store space heterogeneously across the CCN network, and contrast the performance to that of an homogeneous allocation.

[2]: 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.

[3]: Caching scheme will change the original feature of the network in Content Centric Networking (CCN). So it becomes a challenge to describe the caching node importance according to network traffic and user behavior. In this work, a new metric named Request Influence Degree (RID) is defined to reflect the degree of node importance. Then the caching performance of CCN has been addressed with specially focusing on the size of individual CCN router caches. Finally, a newly content store space heterogeneous allocation scheme based on the metric RID across the CCN network has been proposed. Numerical experiments reveal that the new scheme can decrease the routing stretch and the source server load contrasting that of the homogeneous assignment and several graph-related centrality metrics allocations.
A new metric has been presented in [3] , named Request Influence Degree (RID), to reflect the importance of every router along the content delivery path by considering incoming Interest and outgoing Data messages. The number of all requests received by a router from others has been calculated and multiplied by a weight. The weight is computed from miss rates of caches along the path to show the number of requests that should be sent by a to reach an in-network router. An Interest message arrived at a special router has this information of past misses. However, as the current router needs to have knowledge about past routers on the path, the message-passing among routers is increased significantly. The short-term and the long-term parameters presented in this article, are inspired from [1]7 , and the work presented by Rossi and Rossini [1] .