Network Operation Status Evaluation Monitoring System Based on Machine Learning Algorithm

After decades of steady development, the Internet industry has become an important driving force for global economic and social development, and the scale of the network is growing. The evaluation and monitoring system of network operation status plays a vital role in ensuring the normal operation of the network, diagnosing network faults in time, and meeting the service needs of different users. The purpose of this paper is to study the network operation status evaluation and monitoring system based on machine learning algorithm, mainly to expound the background and importance of this topic, as well as the development status at home and abroad, mainly to study the software architecture of the network operation monitoring system in the IP network environment And monitoring architecture design and implementation, further theoretical research on background server load balancing technology, and a load balancing mechanism based on machine learning algorithm is proposed. The database business simulation results show that the database query time of the load balancer based on the genetic algorithm is about 3.3 seconds, while the load balancer based on the machine learning algorithm only needs 2.2 seconds, a reduction of nearly 0.9 seconds. It can be seen that machine learning algorithms also have great advantages in database query business.