基于弹性空间模型的实验室网络数据存储算法  

Laboratory Network Data Storage Algorithm Based on Elastic Space Model

作　　者：万晓云 张泰 程妍[2] WAN Xiao-yun;ZHANG Tai;CHENG Yan(School of Computer Science and Engineering,Hunan University of Information Technology,Changsha Hunan 410151,China;College of Information and Intelligence,Hunan Agricultural University,Changsha Hunan 410128,China)

机构地区：[1]湖南信息学院计算机科学与工程学院,湖南长沙410151 [2]湖南农业大学信息与智能科学技术学院,湖南长沙410128

出　　处：《计算机仿真》2024年第9期368-371,428,共5页Computer Simulation

摘　　要：开放实验室网络系统海量数据会加重云端处理中心负担,降低了数据云存储性能。为解决上述问题,提出一种基于弹性空间模型的实验室网络数据存储算法。数据云存储量大导致数据链路存在冗余问题,为此对开放实验室网络数据实施清洗处理,构建弹性空间模型,通过该模型获取网络感知数据在近邻空间中的波动变化,达到降低网络能耗的目的;依据数据预处理结果,构建网络数据云存储模型,利用该模型对网络数据实施海量云调度转存,实现网络数据云存储。实验结果表明,通过对该方法开展网络数据可用性对比、数据节点均衡性对比,验证了上述方法的可行性强、实用性高。The massive amount of data in the open laboratory network system will increase the burden on the cloud processing center and reduce the performance of data cloud storage.To address the aforementioned issues,a laboratory network data storage algorithm based on an elastic space model was proposed.Due to the redundancy problem caused by large data cloud storage,the data in open laboratory network was cleaned,and then an elastic space model was constructed.Based on this model,the fluctuation of network perception data in the nearest neighbor space was obtained,thus reducing network energy consumption.According to the data preprocessing result,a cloud storage model was constructed to achieve the massive cloud scheduling for network data.The experimental results show that the feasibility and practicality of this method have been verified through comparing network data availability and data node balance.

关 键 词：开放实验室 数据云存储 数据清洗 系统能耗 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]