面向ICN的软件定义遗传蚁群融合路由策略  

Software-defined combination of genetic algorithm and ant colony optimization routing strategy for ICN

作　　者：王晶[1] 周金和[1] WANG Jing;ZHOU Jinhe(School of Information and Communication Engineering,Beijing Information Science&Technology University,Beijing 100101,China)

机构地区：[1]北京信息科技大学信息与通信工程学院,北京100101

出　　处：《北京信息科技大学学报（自然科学版）》2021年第4期32-38,共7页Journal of Beijing Information Science and Technology University

基　　金：国家自然科学基金资助项目(61872044)。

摘　　要：近年来,信息中心网络(information-centric network,ICN)被认为是未来网络的一个有前途的新范式。然而,当前ICN路由的研究大部分是基于局部信息,结合全局信息的研究少之又少。针对这一问题,提出一种面向ICN的软件定义遗传蚁群融合(SDGAAC)路由策略,利用控制器集中控制和全局视图功能,将遗传算法和蚁群算法相结合,根据链路状态和节点畅通程度寻找最优转发路径,并利用节点特征和内容流行度与邻居协作做出缓存决策。仿真结果表明,该策略与现有策略相比,能够有效降低平均请求延迟,提高缓存命中率,均衡网络节点负载。In recent years,information-centric network(ICN)has been regarded as a promising new paradigm for future networks.However,most of the current researches on ICN routing are based on local information,and there are very few researches that combine global information.A software-defined combination of genetic algorithm and ant colony optimization(SDGAAC)routing strategy was proposed for ICN.This strategy uses the centralized controlling and global view function of the controller to get the optimal forwarding path,and uses node characteristics and content popularity with neighbor cooperation to make caching decisions.The simulation results show that this strategy can effectively reduce the average request delay,improve the cache hit rate,and balance the network node load by simulation compared with current strategies.

关 键 词：信息中心网络 软件定义网络 路由策略 缓存策略 遗传蚁群算法 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]