Research And Implementation Of A Transmission Configuration System Backend Of Cloud Resource Management Platform

With the evolution of networks,the new generation of network operation systems is deeply integrated with the network,becoming the core bridge to transform network capabilities into business capabilities.With the continuous convergence of cloud computing and the Internet,comprehensive digital management in cloud network development has become an important issue for operators.This requires operators to deploy network function virtualization and provide flexible resource allocation mechanisms in cloud services.Furthermore,more attention needs to be paid to the predictability,availability and reliability of cloud services in hybrid cloud environments.To improve operational efficiency,it is important to break the old model and achieve it through digital transformation.During this process,the cloud network resource management platform plays a critical role in unifying the storage of various resource data and providing services to external parties.Additionally,the platform carries business data and operational functions related to transmission configuration management.In this paper,the roles of users are divided into business personnel and system administrators.The business logic of the transmission configuration management part is sorted out according to the users’ requirements.Based on the requirement analysis,the design of a transmission configuration management system backend for cloud network resource platform is completed,which supports multiple services,having high availability and reliability,and a clear and simple external interface profile.The system is coded and implemented by Java language and Spring related framework.At present,the system has been put online for trial operation and has achieved the expected results.In this paper,an improved ant colony optimization based on direction vectors is proposed to address the drawback of too slow convergence when the ant colony optimization is applied to solve the shortest path problem in large graphs.The unvisited nodes in the dataset are continued to be divided into dead-end points and true candidate sets,thus shortening the search time.Simulation experimental results on datasets of different sizes prove that the algorithm can effectively improve the convergence of the search paths with better accuracy at the same time.The improved algorithm is applied to the automatic pathfinding problem in the transmission network in this system,and solves the problem of finding the shortest path of two nodes in a large-scale network node.From the pathfinding results and performance indexes,it can be seen that the algorithm can effectively obtain the shortest path between nodes in the transmission network and improve the operation speed,which has positive significance for the operational use of this system.