Automatic Performance Simulation for Microservice Based Applications

As microservices can easily scale up and down to adapt to dynamic workloads, various Internet-based applications adopt the microservice architecture to provide online services. Existing works often model applications’ performance according to historical training data, but they using static models cannot adapt to dynamic workloads and complex applications. To address the above issue, this paper proposes an adaptive automatic simulation approach to evaluate applications’ performance. We first model applications’ performance with a queue-based model, which well represents the correlations between workloads and performance metrics. Then, we predict applications’ response time by adjusting the parameters of the application performance model with an adaptive fuzzy Kalman filter. Thus, we can predict the applications’ performance by simulating various dynamic workloads. Finally, we have deployed a typical microservice based application and simulated workloads in the experiment to validate our approach. Experimental results show that our approach on performance simulation is much more accurate and effective than existing ones in predicting response time.