基于机器学习的分布式系统故障诊断实现方法

Implementation method of fault diagnosis for distributed system based on machine learning

分布式系统规模庞大且结构复杂,传统的运维方式已难以满足其对稳定性和高效率的需求。文章采用Web技术和机器学习算法开发了一种分布式系统的故障诊断实现方法。在Web方面,文章设计并实现了用户管理、日志的采集与管理、故障诊断与可视化、运维管理等功能。在机器学习方面,文章训练并评估了决策树、随机森林、前馈神经网络模型,其中随机森林模型的故障诊断准确率高达95%。该系统不仅有利于故障的快速诊断和解决,降低运维的难度,减少人力资源的消耗,提高运维效率,还具有显著的实用价值和广泛的应用前景。

The distributed system is large in scale and structurally complex,making traditional maintenance methods inadequate to meet its requirements for stability and efficiency.This paper develops a fault diagnosis implementation method for distributed system using Web technology and machine learning algorithm.In terms of Web,this paper designs and implements the functions such as user management,log collection and management,fault diagnosis and visualization,and operation and maintenance management.In terms of machine learning,decision trees,random forests,and feedforward neural network models are trained and evaluated.The fault diagnosis accuracy based on random forest models can achieve up to 95%.This system is not only conducive to rapid fault diagnosis and resolution,reducing the complexity of operations and maintenance,minimizing manpower consumption,improving operational efficiency,but also offering significant practical value and broad application prospects.