基于Adaboost学习的lCN自适应缓存算法

针对信息中心网络(ICN)中缓存内容优化放置的问题，提出一种基于Adaboost学习的自适应缓存算法ACAL.该算法首先将提取的节点和内容数据流作为网络资源，然后利用集成学习算法Adaboost对数据流进行分析挖掘，利用挖掘出的状态属性与缓存匹配之间的函数映射关系对未来时间段内的节点与内容间的匹配关系进行预测，该预测结果用于指导缓存的部署.实验结果表明，ACAL在延时、缓存命中率和链路利用率等指标方面，与CEE策略、LCD策略、prob0.5策略和OPP策略相比有显著的优势.

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

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.