Research On Analysis And Monitoring Of Actor System Based On Message Monitoring

With the increasing complexity of Internet applications,traditional software architectures have been difficult to support the system access pressure.Distributed systems have gradually become mainstream architectures.Message communication has become a widely adopted model based on the distribution of Actor message communication models.The framework has gradually become popular,and the Actor model has become the theoretical basis of many parallel computing systems.However,Systems that use the Actor model cannot avoid failures at runtime.At present,the anomaly detection method for the Actor system is mainly based on the detection of Java code and JVM level.It is difficult to find the message processing exception in the complex Actor system in time,and it is impossible to predict in advance for the system failure.The research on dynamic analysis and monitoring of Actor model based on message monitoring proposed in this paper ca n collect the message data in the Actor system,and use the algorithm of kernel principal component analysis to detect the abnormal state of the program,and use the random forest algorithm to predict the system fault condition.Firstly,the program slice method of bytecode injection is used to obtain the message transfer condition in the Actor system.The kernel principal component analysis m ethod is used to perform data dimensionality reduction and feature extraction through nonlinear mapping,and the K-means algorithm is used for aggregation.Class analysis,using the local outliers factor algorithm to make abnormal point judgments,find the abnormality quickly and effectively,and promptly make an early warning so that the developer can take precautions.Secondly,based on the random forest algorithm,the Actor system fault prediction model uses the random forest algorithm as the prediction model.The message data in the system is used as the prediction data to train the classifier through the random forest algorithm,and the fault prediction model of the Actor system is established,and the accuracy is evaluated.Then,the trained model is used to predict the abnormal state of the system,so as to classify the unknown instances,predict the future abnormal state of the Actor system,and discover potential problems or security threats in the program early.This allows system administrators to have more time to diagnose faults,organize preventive and remedial measures,and prevent maj or production accidents.Finally,based on the Actor system anomaly detection algorithm and fault prediction model,an analysis and monitoring system is developed to analyze the abnormality and predict the fault of the Actor system...