Realization And Optimization Of Server Cluster In Intelligent Hanging Basket Leasing Information Management System

With the comprehensive development of my country’s economy and urbanization,the construction industry is booming,and the construction needs of high-rise buildings and super high-rise buildings are increasing.The exterior wall decoration,maintenance,and cleaning of such buildings require aerial work baskets as construction equipment.The lease,use and management of aerial work gondola equipment is still mainly done offline,which is inefficient and has many potential safety hazards.In recent years,with the rapid development of Internet technology,managers and users of gondola equipment hope to use mobile APP,computer webpages and other computer technologies to realize online equipment management and use processes,which can standardize business processes and It can realize the real-time monitoring of the gondola equipment,and can effectively avoid the occurrence of safety accidents.This paper combines computer technology and network technology to design and develop the backend server cluster software of the intelligent hanging basket leasing information management system,and optimize the server cluster based on development technologies such as Hadoop DFS,multiple linear regression analysis algorithm,Mycat and Redis to improve The operating efficiency and reliability of the system.This article first analyzes the overall requirements of the back-end service of the system based on the actual situation of the business process of the aerial work gondola leasing management,designs a back-end service software architecture scheme based on server clusters,and divides the back-end service software into Web application servers,communication servers,Four functional modules of file server and database.Secondly,the load balancing strategy of the server cluster is studied,and a lightweight response time prediction model is designed based on the multiple linear regression algorithm.The static and dynamic factors affecting the response time are collected and combined with the multiple linear regression algorithm to respond to each node in the cluster.Time is predicted,and the weight of different nodes in the cluster is dynamically adjusted through the predicted response time.Finally,Nginx realizes load balancing of requests according to the node weight.Experiments verify that the load balancing algorithm has better fault tolerance than other algorithms,and can provide fast and stable service response,and can better solve the load balancing problem of server clusters.Then,the background server cluster software of the intelligent hanging basket rental information management system is designed and implemented.The core service software is developed based on Spring Boot’s SSM framework,using My SQL as the database,and implementing a hybrid file server based on HDFS+FTP.The back-end server cluster generally implements functional modules such as hardware communication,message push,user management,project management,hanging basket management,report management,and file management.At the same time,in view of the high access pressure of the database system and the large amount of data,Redis and Mycat technologies are used to realize the optimization of database cache and database sub-table sub-table.Finally,the deployment of the various functional modules of the back-end server cluster is completed.The operation results show that the back-end server cluster has strong scalability,reliability and response speed,and can meet the application requirements of the system.