Detection of microservice-based software anomalies based on OpenTracing in cloud

Today, the noticeable tendency of the software industry to break large software projects into loosely coupled modules through a microservice-based architecture is more than ever. This is because of advantages such as scalability, independence, smaller and faster deployments, improved fault isolation, and flexibility. On the other hand, it should be noted that with the growth of microservice architecture, new complexities have emerged. We need to have a mature DevOps team to handle the complexity involved in maintaining and supporting systems, namely functional and non-functional monitoring (anomaly monitoring and detection). This challenge can lead to a lot of software development time being spent monitoring and identifying anomalies. Existing approaches are not accurate enough to identify anomalies, and if they are able to identify them, they are unable to identify the category of the anomaly. Our approach in this research is to use distributed tracing with the help of machine learning algorithms to identify performance anomalies, the exact location of each anomaly, and predict its category. In this research, we implemented a software based on microservice architecture and then created a variety of anomalies over time (e.g., physical resources, virtual resources, database, application) to be able to evaluate the proposed model. The resulting dataset is publicly available. Our simulation results show that the proposed model is able to accurately identify the anomalies with 98% accuracy and their category with 99% accuracy.