Multidimensional data learning-based caching strategy in information-centric networks
Ling Cai, Xingwei Wang, Jinkuan Wang, Min Huang, Yang Tian · 2017
In-network caching is an important feature of ICN (Information Centric Networking). There are prior arts focusing on designing a highly efficient caching strategy by exploiting either node data or content data respectively. However, simply exploiting these data itself is not enough to reduce the cost of network operation and increase the quality of user experience, as there is no consideration on supplementary action of these data. In this paper, a multidimensional data learning based strategy (MDDL) is proposed to cache the selected content in a few suitable nodes. To understand the current state of node and content, a multidimensional state attribution data model including network, node and content data is proposed. Based on the model, the mapping relationship between the attribution data and the matching relationship value is mined. Utilizing this mapping function, a matching algorithm to predict the matching relationship between the node and the content in the next time period is proposed. In order to improve the accuracy of the matching algorithm, a safe semi supervised support vector machine (S4VM) algorithm is introduced. Simulation results show that MDDL outperforms CEE, BETW and LCD when looking at cost reduction of network operation and enhancement in quality of user experience.