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Adaptive Caching Strategy Based on Big Data Learning in ICN

Cai, L, Wang, X, Li, K, Cheng, H and Cao, J (2018) Adaptive Caching Strategy Based on Big Data Learning in ICN. Journal of Internet Technology, 19 (6). pp. 1677-1689. ISSN 1607-9264

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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.

Item Type: Article
Uncontrolled Keywords: 0805 Distributed Computing, 1005 Communications Technologies, 0806 Information Systems
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: Computer Science & Mathematics
Publisher: Taiwan Academic Network Management Committee
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Date Deposited: 03 Apr 2020 11:49
Last Modified: 04 Sep 2021 07:32
DOI or ID number: 10.3966/160792642018111906005
URI: https://researchonline.ljmu.ac.uk/id/eprint/12649
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