内容中心网络中基于K核分解的缓存决策策略

发布时间：2018-04-26 23:13

本文选题：内容中心网络 + 缓存决策　；参考：《计算机工程》2017年04期

【摘要】：内容中心网络(CCN)默认采用的处理缓存方式存在严重的缓存冗余,而基于介数的缓存决策虽然降低了缓存冗余,但其计算复杂度较高,不适合部署在CCN网络层。为此,提出基于K核分解的缓存决策策略。给出基于K核分解方法的K核值指标定义,将数据对象缓存到请求路径上K核值最高的节点,并给出当K核值相同时依据剩余缓存空间和请求跳数的决策策略。仿真实验结果表明,基于K核分层的缓存决策策略具有较快的收敛速度,在缓存系统稳定时有更高的缓存命中率和更快的缓存响应速度。
[Abstract]:CCNs have serious cache redundancy by default, while the buffer decision based on medium reduces the cache redundancy, but its computational complexity is high, so it is not suitable for deployment in CCN network layer. Therefore, a cache decision strategy based on K kernel decomposition is proposed. The definition of K kernel index based on K kernel decomposition method is given, and the data object is cached to the node with the highest K kernel value in the request path, and the decision strategy based on the residual cache space and the number of request hops is given when the K core value is the same. The simulation results show that the buffer decision strategy based on K-core layer has faster convergence speed, higher cache hit ratio and faster cache response speed when the buffer system is stable.
【作者单位】：中国科学技术大学信息科学技术学院;
【分类号】：TP333;O157.5

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