Caching method and device for content centric networking (CCN)
A content-centric network and cache technology, applied in the computer field, can solve the problems of poor cache diversity, high communication overhead of network nodes, and high cache redundancy, and achieve the effects of reducing overhead, increasing diversity, and reducing redundancy.
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Embodiment 1
[0024] figure 1 The implementation process of the content-centric network caching method provided by the first embodiment of the present invention is shown. For the convenience of description, only the parts related to the embodiment of the present invention are shown, and the details are as follows:
[0025] In step S101, the node centrality of each node in the content-centric network is generated according to a preset calculation formula of node centrality.
[0026] In the embodiment of the present invention, the node centrality reflects the importance of the node in the content-centric network, and the calculation formula of the node centrality is:
[0027] τ(i)=ατ D (i)+βτ C (i)+γτ B (i), where τ(i) is the node centrality of node i in the content-centric network, τ D (i) is the degree centrality of node i, τ C (i) is the close centrality of node i, τ B (i) is the node betweenness of node i, α, β and γ are preset weights, and α+β+γ=1.
[0028] Therefore, in the speci...
Embodiment 2
[0044] figure 2 The structure of the caching device of the content-centric network provided by the fourth embodiment of the present invention is shown. For the convenience of description, only the parts related to the embodiment of the present invention are shown, including:
[0045] The node centrality calculation module 21 is configured to generate the node centrality of each node in the content-centric network according to a preset node centrality calculation formula.
[0046] In the embodiment of the present invention, the node centrality reflects the importance of the node in the content-centric network, and the calculation formula of the node centrality is:
[0047] τ(i)=ατ D (i)+βτ C (i)+γτ B (i), where τ(i) is the node centrality of node i in the content-centric network, τ D (i) is the degree centrality of node i, τ C (i) is the close centrality of node i, τ B (i) is the node betweenness of node i, α, β and γ are preset weights, and α+β+γ=1.
[0048] Therefore,...
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