ConfAdvisor: A performance-centric configuration tuning framework for containers on kubernetes

Abstract

Configuration tuning of software is often a good option to improve application performance without any application code modifications. Although we can casually change configurations, it is not easy to apply optimal configurations, as optimal configurations require deep knowledge of the underlying system. This is problematic because applications with suboptimal configuration result in poor performance. As container and container management systems have emerged as an application platform on the cloud, configuration tuning becomes even more challenging because containers add more complexity to the application performance. We need to consider not only fundamental misconfiguration but also container image verification, deployment configuration, application characteristics awareness based on metrics and logs. Although previous knowledge regarding how we should tune configurations for a system software is sometimes available, knowledge about performance tuning practices is neither normalized nor reusable to expand on any advice for misconfiguration to the containers. Even in the cloud-native environment, there is no centralized service to deliver knowledge continuously to application containers nor a framework to develop a misconfiguration fix rule for a container throughout its lifetime. In this paper, we propose a performance-centric configuration tuning framework for containers on Kubernetes, named ConfAdvisor, that enables containers to achieve a higher performance by validating various misconfigurations adaptively. ConfAdivsor gives config tuning advice to application containers, images, and Kubernetes specs and also provides a development framework to build configuration validation rules. We present the design of ConfAdvisor and provide several case studies to tune application containers in the real world.