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An adaptive cache management approach in ICN with pre-filter queues

Authors :
Yao Wang
Dapeng Man
Mohsen Guizani
Xiaojiang Du
Yang Wu
Qi Lu
Publication Year :
2020
Publisher :
Elsevier B.V., 2020.

Abstract

In Information-Centric Networking (ICN), transmission does not depend on the ends of communication, but on the content itself. In-network cache plays an important role in ICN, it empowers nodes in ICN better mobility. It also shortens serving path, lightens load of server, reduces traffic and time delay, its efficiency seriously affects performance of entire network, and thus cache management in ICN draws much attention of researchers recently. Although there are a lot approaches have been proposed, a cache management scheme with better adaptability and less cost remains to be studied. To this end, we propose State-value-and-Cache-rate-Method (SCMethod) to manage cache resources in ICN, which comprises cache deployment policy and cache replacement policy. In cache level, we add pre-filter queues in front of cache queue to filter out popular content to store in cache. In the perspective of node, according to factors that node's state and relative location on data forwarding path, we define node state and cache rate to select nodes with maximum of state value or minimum cache rate to cache content, and effectively mitigate data redundancy. In order to increase dynamics and adaptive of system for mobility of nodes, we employ cache hit ratio as feedback to dynamically adjust the number of pre-filter queues in every node. With pre-filter queues, we improve Least Recently Used (LRU) cache replacement method. We conduct extensive experiments in simulator Icarus (Saino et al., 2014) with both tree topology and realistic internet topologies, define four metrics to quantitatively evaluate performance of SCMethod and verify its efficacy of reducing latency and load. Simulation results demonstrate that SCMethod we proposed outperform several classic cache schemes of ICN. This document is the results of the research project funded by the National Natural Science Foundation of China , grant number 61771153 and 61831007 .

Details

Language :
English
Database :
OpenAIRE
Accession number :
edsair.doi.dedup.....9db6bc05f4610cd57736b1727ff4b34f