Fast and Efficient Performance Tuning of Microservices

Abstract

The microservice architecture is being increasingly adopted. Microservices often rely on containerization technology, facilitating agile development and permitting flexible deployment on cloud platforms. Many microservice applications are interactive. Consequently, there is a need for pre-deployment performance tuning techniques to ensure that an application will meet its end user response time requirements post-deployment. Additionally, the tuning process should be efficient, i.e., allocate just enough resources to minimize costs in cloud-based deployments. Furthermore, the tuning process needs to be fast to facilitate agile deployments. We design and evaluate a technique called MOAT (Microservice Application Performance Tuner) that embodies these requiremenis. MOAT conducts iterative performance tests to determine resource allocations for the individual microservices in an application for any given workload. It exploits a novel optimization technique that identifies resource allocations while requiring only a limited number of performance tests to explore the tuning space. Validation using an experimental system shows that MOAT outperforms a competing approach based on Bayesian optimization in terms of both solution speed and resource allocation efficiency.