Application of Autonomous Monitoring Method Based on Distributed Environment Deployment in Network Fault

Abstract

With the rapid development of information technology and the increasing demand for computing, the scale of cloud environment and distributed deployment is becoming larger and larger. Often, due to the outage of a node, a series of chain reaction problems occur, which makes the production environment suffer unpredictable losses. All kinds of uncertainties, randomness, concurrency and diversity exist in the above-mentioned problems. Because of these factors, it brings great troubles to the relevant staff to locate the causes of network failure. In order to solve the problem of network failure in large-scale data network, it is difficult to locate the fault location accurately, and how to give users accurate feedback on the causes of network failure. In this paper, we construct a random forest algorithm, which is based on agent distributed in each device. The system is trained continuously by machine learning, and the samples are classified by decision tree classifier. Each decision tree represents a judgment of the cause of network failure. When an error occurs, the agent collects and pre-processes the device data information according to the algorithm and feeds it back to the controller for aggregation. Then, the analyzer is used to match, judge and find out the corresponding network faults that meet the requirements. The practical application results show that the design model is feasible, which significantly improves the efficiency of problem tracking and the accuracy of problem feedback screening, but also provides strong support for the follow-up intelligent operation and maintenance.