摘要： 现有的命名数据网络缓存策略较少考虑节点间的缓存协作,易导致相邻节点缓存相同的内容,降低网络整体缓存效率。为此,构建一种全域协作缓存模型,针对模型求解难度较大的特点,提出一种基于灰狼优化(GWO)算法的预留协作缓存算法RCC。使用sigmoid函数将GWO算法连续位置更新函数二进制化,并编写位置检测与更正算法,以修正灰狼非法位置。仿真结果表明,相较于CVX优化器,该算法能以较小的时间和内存求得问题的近似最优解,且与传统缓存策略相比,其能取得较高的缓存命中率及较小的请求平均跳数。

关键词: 命名数据网络, 视频, 缓存策略, 域内协作, 缓存冗余, 灰狼优化算法

Abstract: In-network caching is one of the key aspects of Named Data Networking(NDN).The existing caching strategy of NDN takes insufficient consideration on caching cooperation among nodes,which can easily lead to the adjacent node to cache the same content and reduce the overall caching efficiency.To solve this problem,this paper builds a cooperative caching model employed in global domain,it is difficult to solve this model,so an algorithm of reservation cooperative cache based on Grey Wolf Optimization(GWO) algorithm is proposed which named RCC.The algorithm uses the sigmoid function to binarize the continuous position update function of the GWO algorithm and uses the position detection and correction algorithm to correct the wolf′s illegal position.Simulation results show that,compared with the CVX optimizer,the RCC algorithm can obtain the approximate optimal solution of the problem with less time and memory,and compared with the traditional caching strategy,the RCC algorithm achieves higher caching hit rate and smaller average hops.

Key words: Named Data Networking(NDN), video, caching strategy, inner domain cooperation, caching redundancy, Grey Wolf Optimization(GWO) algorithm

中图分类号: 

• TP391