An autonomic election algorithm based on emergence in natural systems
Integrated Computer-Aided Engineering - Autonomous Computing
Understanding and dealing with operator mistakes in internet services
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Automatic misconfiguration troubleshooting with peerpressure
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Automatic configuration of internet services
Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007
Diagnosing misconfiguration with dynamic detection of configuration invariants
HotDep'07 Proceedings of the 3rd workshop on on Hot Topics in System Dependability
SPIKE: best practice generation for storage area networks
SYSML'07 Proceedings of the 2nd USENIX workshop on Tackling computer systems problems with machine learning techniques
Towards automatic reverse engineering of software security configurations
Proceedings of the 15th ACM conference on Computer and communications security
Constraint violation detection: a fundamental part of software cybernetics
COMPSAC-W'05 Proceedings of the 29th annual international conference on Computer software and applications conference
MassConf: automatic configuration tuning by leveraging user community information
Proceedings of the 2nd ACM/SPEC International Conference on Performance engineering
Context-based online configuration-error detection
USENIXATC'11 Proceedings of the 2011 USENIX conference on USENIX annual technical conference
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Managing the configuration of computer systems today is a difficult task. Too easily, a computer user or administrator can make a simple mistake or lapse and misconfigure a system, causing instabilities, unexpected behavior, and general unreliability. Bugs in software that changes these configurations, such as installers, only worsen the situation. A self-managing configuration system should be continuously monitoring itself for invalid settings, preventing the bugs from harming the system. Unfortunately, while there are many constraints which can differentiate between valid and invalid settings, few of these constraints are explicitly written down, much less written down in a form usable by an automatic monitor. We propose an approach to automatically infer these correctness constraints based on samples of known good configurations. In this paper we present Glean, a system for analyzing the structure of configurations and automatically inferring four types of correctness constraints on that structure.