Fog computing based content-aware taxonomy for caching optimization in information-centric networks

Meng Wang, Jun Wu, Gaolei Li, Jianhua Li, Qiang Li

Abstract: Traditional internet architecture is challenged and it cannot satisfy the growing demand of todays networks. Information-centric Networks (ICN) is regarding as a promising replacement to meet this trend. The main characteristic in ICN is Content Store (CS), which is used to enable users to retrieve data from nearby nodes instead of remote server. However, with the limited storage capacity of routers, we cannot make the most use of the concept of CS. In this paper, we proposed a novel framework using fog computing as a middle level to communicate both with underlying network and ICN global network, where data is preprocessed and classed in fog node before transferring to ICN. In this way, we can reduce the total number of caching content in network by labeling the dynamic data and user-shareable data. We proved that with limited storage capacity of content store, we cannot profit from in-network caching. This result suggests the necessity of proposed framework.