ConfEx: Towards Automating Software Configuration Analytics in the Cloud

Abstract

Modern cloud applications are designed in a highly configurable way to ensure increased reusability and portability. With the growing complexity of these applications, configuration errors (i.e., misconfigurations) have become major sources of service outages and disruptions. While some research has so far focused on detecting errors in configurations that are represented as well-structured key-value pairs, the configurations of cloud applications are typically stored in text files with application-specific syntax and in unlabeled file system locations, limiting the use of existing error detection tools. This paper introduces ConfEx, a framework that enables discovery and extraction of text-based configurations in multi-tenant cloud platforms and cloud image repositories for configuration analysis and validation. ConfEx uses a novel vocabulary-based technique to identify text-based configuration files in cloud system instances with unlabeled content, and leverages existing configuration parsers to extract the information in these files. We show that ConfEx achieves over 98% precision and recall in identifying configuration files on 3893 popular Docker Hub images and we also demonstrate a use case of ConfEx for detecting injected misconfigurations via outlier analysis.