作　　者： (许慧青）; (王高才）; (闵仁江）;

机构地区： 广西大学计算机与电子信息学院,南宁530004

出　　处： 《计算机科学》 2017年第8期76-81,106,共7页

摘　　要： 当前大多内容中心网络(Content-Centric Network,CCN)缓存决策策略研究都没有综合考虑请求热点、网络能耗、内容流行度和节点协同等相关要素。因此提出一种基于内容流行度的协同缓存策略来优化内容中心网络的能耗。该策略将CCN的一个自治区域网络中的所有内容路由器节点当作一个协同缓存组,并把协同缓存组中每个节点的缓存容量分为两部分,一部分用于自身节点和其他节点协同缓存内容;另一部分用于自身节点独立缓存本地最流行的内容,以提高协同缓存组中内容副本种类的多样性,从而减少网络中内容的重复传输,实现网络能耗的优化。建立相关的能耗优化模型,采用一种改进的遗传算法求解出该协同缓存组能耗优化问题的最优解。实验结果表明,与相关文献中的缓存决策策略相比,所提策略可以有效地降低CCN的能耗,提高其可扩展性,进而指导CCN的演化和部署。 Now most researches about caching decisions of CCN（Content-Centric Networking）don＇t synthetically considerthese related elements such as request hot,network energy consumption,content popularity,nodes cooperation and so on.This article proposed an energy-efficient caching strategy based on coordinated caching.This strategy regards the content routers in an autonomous domain as a cooperative cache group,and divides the cache capacity of every node in collaborative cache group into two sections,one for cooperative caching content and the rest for independently caching the local most popular contents,to improve content replicas diversity in cooperative caching group and reduce duplication of content transmission,enabling to minimize network energy consumption.We established relevant energy consumption model and used an advanced genetic algorithm（GA）to solve the optimization energy problem in this cooperative cache group.The results show that the proposed strategy to contrast current strategies can effectively reduce the energy consumption of CCN,which can improve its scalability and guide the evolution and deployment of CCN.

关 键 词： 内容中心网络 能耗优化 协同缓存 遗传算法