Abstract

Misconfiguration is frequently cited as a leading cause of service disruptions and outages. To prevent misconfiguration, we introduce network contracts—lightweight configuration checks that run efficiently, localize errors to specific lines, and require no heavyweight modeling of network protocols. We develop a tool Concord to learn contracts automatically from example network configurations. By checking these learned contracts against new or changed configurations, Concord finds likely configuration bugs before they can impact the network. Key to our approach is a scalable algorithm for learning “relational” contracts that capture complex dependencies between configuration settings. We deployed Concord as part of a cloud-based configuration management service and evaluated its scalability, coverage, precision, and utility on two large real-world configuration datasets.

BibTeX Citation

@inproceedings{10.1145/3767295.3769338,
    author = {Beckett, Ryan and
              Yan, Francis Y. and
              Pocha, Raghunadha Reddy and
              Raj, Vineesh V. and
              Shaik, Ayyub and
              Kakarla, Siva Kesava Reddy},
    title = {Concord: Learning Network Configuration Contracts},
    year = {2026},
    isbn = {9798400722127},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    url = {https://doi.org/10.1145/3767295.3769338},
    doi = {10.1145/3767295.3769338},
    booktitle = {Proceedings of the 21st European Conference on Computer Systems},
    pages = {801–818},
    numpages = {18},
    keywords = {configuration validation, misconfiguration detection, network reliability, association rule learning},
    location = {McEwan Hall/The University of Edinburgh, Edinburgh, Scotland UK},
    series = {EUROSYS '26}
}