基于Kalman算法的大数据存储架构可扩展性优化算法  

A scalability optimization algorithm for big data storage architecture based on Kalman algorithm

作　　者：韩镇阳 张磊 任冬 Han Zhenyang;Zhang Lei;Ren Dong(Shanxi Provincial Corps of the Chinese People′s Armed Police Force,Xi′an 710116,China)

机构地区：[1]武警陕西省总队,陕西西安710116

出　　处：《网络安全与数据治理》2023年第11期25-28,共4页CYBER SECURITY AND DATA GOVERNANCE

摘　　要：为了优化大数据存储架构可扩展性能,提高大数据架构资源利用率,通过引入Kalman算法设计了一种大数据存储架构可扩展性优化算法。首先,综合考虑大数据存储架构与多核环境内存布局之间的兼容性,设计架构内存布局。其次,设计分布式共享内存协议,确保各个进程在访问共享内存时能够正确地协同工作,提高存储架构的容错性。在此基础上,利用Kalman算法,动态调整存储节点的负载,进而优化大数据存储架构,以提高其可扩展性。实验结果表明,应用该算法后,大数据存储架构的资源利用率始终高于对照组,均达到了96%以上,最高达到了98%,架构可扩展性优化效果显著,服务器资源利用更充分,大规模数据处理更高效。In order to optimize the scalability performance of big data storage architecture and improve the resource utilization of big data architecture,a Kalman algorithm was introduced to design a scalability optimization algorithm for big data storage architecture.Firstly,considering the compatibility between big data storage architecture and multi core environment memory layout,design the architecture memory layout.Secondly,design a distributed shared memory protocol to ensure that various processes can work together correctly when accessing shared memory,and improve the fault tolerance of the storage architecture.On this basis,the Kalman algorithm is used to dynamically adjust the load of storage nodes and optimize the big data storage architecture to improve its scalability.The experimental results show that the resource utilization rate of the big data storage architecture is consistently higher than that of the control group,reaching over 96%,with a maximum of 98%.The scalability optimization effect of the architecture is significant,and the utilization of server resources is more sufficient,enabling more efficient processing of large-scale data.

关 键 词：KALMAN算法 大数据存储架构 可扩展性优化 共享内存协议 节点负载 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]