Network Monitoring and Performance Optimization of Large-scale Distributed System Based on Big Data Algorithm

This paper aims to discuss the network monitoring and performance optimization of large-scale distributed systems based on big data algorithm. In this paper, firstly, the network monitoring data of large-scale distributed systems are collected and processed and analyzed by using big data technology. By digging deeply into the useful information in these data, the state characteristics and behavior patterns of the system operation can be obtained. Secondly, the multi-dimensional index and machine learning algorithm are combined to cluster and classify the data to identify the root causes of network delay, congestion and failure. Finally, the optimization algorithm is used to rationally configure and manage the network resources to realize the performance optimization of the system. The simulation results show that the method proposed in this paper can effectively reduce the influence of network delay, congestion and failure, and improve the availability and performance of the system. Compared with traditional monitoring and performance optimization methods, the method proposed in this paper has higher accuracy, robustness and strong adaptability. This study can also provide some reference for other similar problems.