An Evolutionary Study of Configuration Design and Implementation in Cloud Systems

TL;DR

This study investigates how developers evolve configuration design and implementation in cloud systems through a detailed analysis of version-control history, revealing insights to improve configuration practices and reduce misconfigurations.

Contribution

It provides the first empirical analysis of configuration evolution in cloud systems, highlighting developer practices and challenges in configuration design and implementation.

Findings

Developers frequently revise initial configuration decisions.

Configuration-related commits are spread over 2.5 years in large projects.

Insights suggest new techniques for proactive misconfiguration reduction.

Abstract

Many techniques were proposed for detecting software misconfigurations in cloud systems and for diagnosing unintended behavior caused by such misconfigurations. Detection and diagnosis are steps in the right direction: misconfigurations cause many costly failures and severe performance issues. But, we argue that continued focus on detection and diagnosis is symptomatic of a more serious problem: configuration design and implementation are not yet first-class software engineering endeavors in cloud systems. Little is known about how and why developers evolve configuration design and implementation, and the challenges that they face in doing so. This paper presents a source-code level study of the evolution of configuration design and implementation in cloud systems. Our goal is to understand the rationale and developer practices for revising initial configuration design/implementation decisions, especially in response to consequences of misconfigurations. To this end, we studied 1178 configuration-related commits from a 2.5 year version-control history of four large-scale, actively-maintained open-source cloud systems (HDFS, HBase, Spark, and Cassandra). We derive new insights into the software configuration engineering process. Our results motivate new techniques for proactively reducing misconfigurations by improving the configuration design and implementation process in cloud systems. We highlight a number of future research directions.