Usable declarative configuration specification and validation for applications, systems, and cloud

Baset, Salman; Suneja, Sahil; Bila, Nilton; Tuncer, Ozan; Isci, Canturk

doi:10.1145/3154448.3154453

Cited by 22 publications

(13 citation statements)

References 11 publications

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“…Analysis of software configurations can be used both to detect misconfigurations and to gain insight on user configuration practices. Existing automated misconfiguration detection techniques that can be applied as part of ConfEx include outlier value detection [15], parameter type inference [14], [31], rule-based validation [32], [33], parameter correlation analysis [17], matching configuration parameters with the parameters in the source code for source-based analysis [34]. We provide examples of how two of these techniques could be used during this phase in Sec.…”

Section: Discussionmentioning

confidence: 99%

ConfEx: Towards Automating Software Configuration Analytics in the Cloud

Tuncer

Bila²,

Duri³

et al. 2018

2018 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W)

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Modern cloud services have complex architectures, often comprising many software components, and depend on hundreds of configurations parameters to function correctly, securely, and with high performance. Due to the prevalence of open-source software, developers can easily deploy services using third-party software without mastering the configurations of that software. As a result, configuration errors (i.e., misconfigurations) are among the leading causes of service disruptions and outages. While existing cloud automation tools ease the process of service deployment and management, support for detecting misconfigurations in the cloud has not been addressed thoroughly, likely due to the lack of frameworks suitable for consistent parsing of unstandardized configuration files. This paper introduces ConfEx, a framework that enables discovery and extraction of text-based software configurations in the cloud. ConfEx uses a novel vocabulary-based technique to identify configuration files in cloud system instances with unlabeled content. To extract the information in these files, ConfEx leverages existing configuration parsers and post-processes the extracted data for analysis. We show that ConfEx achieves over 99% precision and 100% recall in identifying configuration files on 7805 popular Docker Hub images. Using two applied examples, we demonstrate that ConfEx also enables detecting misconfigurations in the cloud via existing tools that are designed for configurations represented as key-value pairs, revealing 184 errors in public Docker Hub images.

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Section: Discussionmentioning

confidence: 99%

ConfEx: Towards Automating Software Configuration Analytics in the Cloud

Tuncer

Bila²,

Duri³

et al. 2018

2018 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W)

Self Cite

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“…To make progress, a comprehensive study of configuration dependencies is needed. Better understanding would significantly benefit existing configuration tooling (e.g., for misconfiguration detection and diagnosis), reliability engineering (e.g., configuration correctness rule engineering [11,18,19]), configuration-aware testing [20,21,22,23,24,25,26,27,28,29,30], and customer support and documentation [31,16].…”

Section: Table Of Contentsmentioning

confidence: 99%

“…cDep can provide dependency information to enhance misconfiguration injection testing [12,70] configuration checking/validation [2,11,75,76,77,18,19], configuration-aware software testing [20,21,22,23,24,25,26,27,28,29,30], and many others [81].…”

Section: Eliminating Configuration Dependenciesmentioning

confidence: 99%

“…The prevalence and severity of software misconfigurations have driven the design and development of a number of detection and diagnosis techniques [12,2,13,14,15,75,82,83,76,77,84,11,18,19,85]. Detection aims at detecting misconfiguration before deployment, while diagnosis identifies root-causes of misconfigurations that caused failures, performance issues, and incorrect results.…”

Section: Chapter 8: Related Workmentioning

confidence: 99%

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Understanding and discovering software configuration dependencies in cloud and datacenter systems

Chen

Wang

Legunsen

et al. 2020

Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Softw

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A large percentage of real-world software configuration issues, such as misconfigurations, involve multiple interdependent configuration parameters. However, existing techniques and tools either do not consider dependencies among configuration parameters-termed configuration dependencies-or rely on one or two dependency types and code patterns as input. Without rigorous understanding of configuration dependencies, it is hard to deal with many resulting configuration issues.This thesis presents our study of software configuration dependencies in 16 widely-used cloud and datacenter systems, including dependencies within and across software components. To understand types of configuration dependencies, we conduct an exhaustive search of descriptions in structured configuration metadata and unstructured user manuals. We find and manually analyze 521 configuration dependencies. We define five types of configuration dependencies and identify their common code patterns. We report on consequences of not satisfying these dependencies and current software engineering practices for handling the consequences.We mechanize the knowledge gained from our study in a tool, cDep, which detects configuration dependencies. cDep automatically discovers five types of configuration dependencies from bytecode using static program analysis. We apply cDep to the eight Java and Scala software systems in our manual study. cDep finds 87.9% (275/313) of the related subset of dependencies from our study. cDep also finds 448 previously undocumented dependencies, with a 6.0% average false positive rate. Overall, our results show that configuration dependencies are more prevalent and diverse than previously reported and should henceforth be considered a first-class issue in software configuration engineering.

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“…It has been a pioneering platform that puts forward the declarative configuration management approach for management of container-based distributed applications. Declarative configuration management entails that a user-specified, desired system state is enforced upon an actual system by a control loop that detects differences between desired and actual state [18]. In Kubernetes, the desired state is specified and managed using a RESTful API and is composed of various types of resources such as containers, nodes and services.…”

Section: A Kubernetesmentioning

confidence: 99%