基于改进蚁群算法的网络缓存资源寻址仿真
Simulation of Network Cache Resource Addressing Based on Improved Ant Colony Algorithm
蒋成 1郭向坤2
作者信息
- 1. 湖北工程学院计算机与信息科学学院,湖北 孝感 432000
- 2. 沈阳理工大学信息科学与工程学院,辽宁 沈阳 110159
- 折叠
摘要
网络信息呈海量增加,资源数据传输频率逐渐加快,且所有用户在同样的时间占用不同的带宽资源,因此网络资源寻址难度较大.为了提高数据传输的准确度,确保集中访问的安全性,提出基于改进蚁群算法的网络缓存资源寻址方法.利用编码技术设计出编码策略,锁定寻址目标.通过改进蚁群算法制定出约束条件下的路由路径.结合约束条件、云信任度评估准则构建云信任度寻址模型,对路由路径中的节点完成表达、连接、管理和识别等处理,完成网络缓存资源的寻址.仿真结果表明,所提方法应用下网络数据存储开销不超过 500MB,网络缓存资源寻址耗时平均为 36.07ms,路径长度在 10bit之内,测试所得数据均说明与现在方法相比,研究方法具有明显的应用优势.
Abstract
In order to improve the accuracy of data transmission and ensure the security of centralized access,an addressing method for network cache resources based on an improved ant colony algorithm was proposed.At first,the coding technology was used to design a coding strategy for locking the addressing target.Then,the routing path under constraints was formulated by improving the ant colony algorithm.Combined with the constraints and cloud trust eval-uation criteria,a cloud trust addressing model was built.After the expression,connection,management and identifica-tion of nodes in the routing path,the network cache resource addressing was completed.Simulation results show that the network data storage overhead is less than 500MB after using the proposed method.The average addressing time of network cache resource is 36.07ms,and the path length is within 10bit.The test data prove the obvious application advantages of the method.
关键词
蚁群算法/先验知识/云信任度评估准则/信任陡度函数/云信任寻址模型Key words
Ant colony algorithm/Prior knowledge/Cloud trust evaluation criteria/Trust steepness function/Cloud trust addressing model引用本文复制引用
基金项目
湖北省教育科学规划课题(2021GB072)
出版年
2024
NETL
NSTL
万方数据