A Real-time, Scalable Monitoring and User Analytics Solution for Microservices-based Software Applications
Cathy H Zhang, M. Omair Shafiq · 2022 IEEE International Conference on Big Data (Big Data) · 2022
Monitoring the execution of distributed microservices-based software applications is a complex task. As more and more institutions conduct business in a distributed environment, a large amount of user data and transaction data are generated at an accelerated rate in such an environment. As a result, it becomes a big challenge to carry out user analytics with user interaction and transaction data in real-time. Monitoring is one of the most important approaches to getting instant situations of users and transactions. Ideally, the goal of monitoring is to carry out analysis in real-time and be highly scalable with comprehensive analysis and predictions on user interaction and transaction data to gain deeper and facilitate decision-making for the stakeholders. Therefore, we concentrate on what data can be captured to gain insight into users using the platform and how we can capture such data effectively to learn application usage patterns. To achieve this goal, (1) we present how different events data be captured from execution logs of software applications in distributed microservices, and (2) we utilize machine learning methods to predict usage patterns which are generated from interaction between user and system. For example, we show how error types can be predicted in real-time using machine learning and then enable real-time monitoring. The capability to perform analysis based on the continuously growing data volume is considered to be our solution's scalability characteristic. Microservices are used as containers that are managed by Kubernetes. Event logs generated in each microservice are the important data source for executing monitoring. The experiment shows our system can monitor user actions and transactions in real time and that the model capacity is scalable.