Diagnosing architectural run-time failures. Casanova, P., Garlan, D., Schmerl, B., & Abreu, R. In 2013 8th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS), pages 103–112, May, 2013. ISSN: 2157-2321doi abstract bibtex Self-diagnosis is a fundamental capability of self-adaptive systems. In order to recover from faults, systems need to know which part is responsible for the incorrect behavior. In previous work we showed how to apply a design-time diagnosis technique at run time to identify faults at the architectural level of a system. Our contributions address three major shortcomings of our previous work: 1) we present an expressive, hierarchical language to describe system behavior that can be used to diagnose when a system is behaving different to expectation; the hierarchical language facilitates mapping low level system events to architecture level events; 2) we provide an automatic way to determine how much data to collect before an accurate diagnosis can be produced; and 3) we develop a technique that allows the detection of correlated faults between components. Our results are validated experimentally by injecting several failures in a system and accurately diagnosing them using our algorithm.
@inproceedings{casanova_diagnosing_2013,
title = {Diagnosing architectural run-time failures},
doi = {10.1109/SEAMS.2013.6595497},
abstract = {Self-diagnosis is a fundamental capability of self-adaptive systems. In order to recover from faults, systems need to know which part is responsible for the incorrect behavior. In previous work we showed how to apply a design-time diagnosis technique at run time to identify faults at the architectural level of a system. Our contributions address three major shortcomings of our previous work: 1) we present an expressive, hierarchical language to describe system behavior that can be used to diagnose when a system is behaving different to expectation; the hierarchical language facilitates mapping low level system events to architecture level events; 2) we provide an automatic way to determine how much data to collect before an accurate diagnosis can be produced; and 3) we develop a technique that allows the detection of correlated faults between components. Our results are validated experimentally by injecting several failures in a system and accurately diagnosing them using our algorithm.},
booktitle = {2013 8th {International} {Symposium} on {Software} {Engineering} for {Adaptive} and {Self}-{Managing} {Systems} ({SEAMS})},
author = {Casanova, Paulo and Garlan, David and Schmerl, Bradley and Abreu, Rui},
month = may,
year = {2013},
note = {ISSN: 2157-2321},
keywords = {Cognition, Computational modeling, Databases, Fault diagnosis, Monitoring, Probes, Web servers},
pages = {103--112},
}
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