Parallelizing Automatic Model Management System for AIOps on Microservice Platforms

Ruibo Chen, Wenjun Wu

Abstract: With the gradual increase in the scale of applications based on microservice architecture, the complexity of system operation and maintenance is also significantly increasing. The emergence of AIOps makes it possible to automatically detect the state, allocate the resources, warn and detect the anomaly of the system through some machine learning models. Given dynamic online workloads, the running state of a production microservice system is constantly in flux. Therefore, it is necessary to continuously train, encapsulate and deploy models based on the current system status, so that the AIOps model can dynamically adapt to the system environment. To address this problem, this paper proposes a model management pipeline framework for AIOps on microservice platforms, and implements a prototype system based on Kubernetes to verify the framework. The system consists of three components: model training, model packaging and model deploying. Parallelization and parameter search are introduced in the model training process to support rapid training of multiple models and automated model hyperparameter tuning. Rapid deployment of models is supported by the model packaging and deploying components. Experiments were performed to verify the prototype system, and the experimental results illustrate the feasibility of the proposed framework. This work provides a valuable reference for the construction of an integrated and streamlined AIOps model management system.