Journal of Northeastern University Natural Science ›› 2019, Vol. 40 ›› Issue (1): 21-25.DOI: 10.12068/j.issn.1005-3026.2019.01.005

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Adaptive Caching Algorithm Based on Adaboost Learning for Information Centric Networking(ICN)

CAI Ling1, WANG Jin-kuan2, WANG Xing-wei3, HU Xi4   

  1. 1. School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China; 2. School of Information Science & Engineering, Northeastern University, Shenyang 110819,China; 3. School of Software, Northeastern University, Shenyang 110169, China; 4. Computing Center, Northeastern University at Qinhuangdao,Qinhuangdao 066004,China.
  • Received:2017-05-24 Revised:2017-05-24 Online:2019-01-15 Published:2019-01-28
  • Contact: CAI Ling
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Abstract: In order to optimize the cache placement in ICN(information centric networking), an ACAL(adaptive caching algorithm based on Adaboost learning) algorithm was proposed. According to the algorithm, first, the extracted data flow including node data and content data was employed as the network resources, then the ensemble learning algorithm Adaboost was used to analyze and mine the data flow, and the mapping relationship between the state attribution data and the matching relationship value was utilized to predict the matching relationship between the node and the content in next period. Finally, the matching relationship algorithm was used to guide the cache placement. The simulation experiments demonstrate that the proposed ACAL, compared with CEE, LCD, prob0.5 and OPP yields a significant performance improvement, such as delay, hit rate and average link utilization.

Key words: information centric networking(ICN), caching network, caching strategy, learning algorithm, Adaboost algorithm

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