Micro-service intelligent monitoring method for abnormal propagation

Abstract

The invention relates to a micro-service intelligent monitoring method for abnormal propagation, which comprises the following steps of: monitoring service calling information based on an agent technology, and establishing a micro-service calling topological graph to describe an abnormal propagation relationship among micro-services; a Lasso regression modeling interface is adopted to call correlation with measurement, and abnormal micro-service is detected by monitoring the change of a correlation model; based on the PageRank algorithm, the abnormal degree of the micro-service and the callinginterface of the micro-service is evaluated, transparent service monitoring is achieved, automatic metric value prediction is achieved to find abnormal service, and the abnormal degree of nodes in agraph is intelligently evaluated to detect the problem root cause.

Description

technical field [0001] The invention relates to a fault diagnosis method for a microservice software system, in particular to a microservice intelligent monitoring method for abnormal propagation, which belongs to the field of software technology. Background technique [0002] Monolithic architecture and SOA software architecture are commonly used by software companies. After more than ten years of development, software systems have become extremely complex, with low scalability and maintainability, and enterprises are burdened with heavy technical debt. Today's Internet competition is fierce, and user needs and market environments are constantly changing. In the face of today's Internet applications, the scalability and flexibility of traditional software architectures are obviously insufficient, while the cost of design, development, testing, and operation and maintenance is low. A significant increase. Therefore, the concept of microservice was proposed. Microservice is ...

AI Technical Summary

Problems solved by technology

The monitoring fault diagnosis method based on metrics and logs is simple to implement, but it cannot reflect the overall state of the system and cannot track business flows. The level of fault location is usually service components. In complex microservice interaction relationships, administrators will spend a lot of time time to find and locate the problem; and the monitoring and diagnosis method based on distributed request tracing monitors the trace of the request through logs or embedded code as a reference for fault diagnosis, but the scalability of this method is low, and it cannot be applied to the application. Transparent, and does not take into account the problem of exception propagation

Images