Adaptive caching strategy based on big data learning in ICN

Ling Cai, Xingwei Wang, Keqin Li, Hui Cheng, Jiannong Cao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

3 Citations (Scopus)


In-network caching, a typical feature of information centric networking (ICN) architecture, has played an important role on the network performance. Existing caching management strategies mainly focus on minimizing the redundancy content by exploiting either node data or content data respectively, which may not lead to effectively improve the caching performance, as there is no consideration on supplementary action of these two types of data. In this paper, the correlation between node data and content data brought by the big data are analyzed and mined to determine whether the selected content are cached in a few suitable nodes, and a Big data driven Adaptive In-network Caching management strategy (BAIC) is proposed. Driven by the current state of node and content, a novel multidimensional state attribution data model including network, node and content data is proposed. Based on the data model, the mapping relationship between the status data and the matching relationship value is further analyzed and mined. And then utilizing this mapping relationship function, the matching algorithm to predict the matching relationship between the node and the content in the next time period is proposed. The simulation experiments demonstrate that the proposed BAIC has significantly improved the network performance.

Original languageEnglish
Pages (from-to)1677-1689
Number of pages13
JournalJournal of Internet Technology
Publication statusPublished - 1 Jan 2018


  • Big data learning
  • Caching
  • Information centric networking

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications


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