Generic adaptive monitoring based on executed architecture runtime model queries and events (bibtex)
by ,
Abstract:
Monitoring is a key functionality for automated decision making as it is performed by self-adaptive systems, too. Effective monitoring provides the relevant information on time. This can be achieved with exhaustive monitoring causing a high overhead consumption of economical and ecological resources. In contrast, our generic adaptive monitoring approach supports effectiveness with increased efficiency. Also, it adapts to changes regarding the information demand and the monitored system without additional configuration and software implementation effort. The approach observes the executions of runtime model queries and processes change events to determine the currently required monitoring configuration. In this paper we explicate different possibilities to use the approach and evaluate their characteristics regarding the phenomenon detection time and the monitoring effort. Our approach allows balancing between those two characteristics. This makes it an interesting option for the monitoring function of self-adaptive systems because for them usually very short-lived phenomena are not relevant.
Reference:
Generic adaptive monitoring based on executed architecture runtime model queries and events (Thomas Brand, Holger Giese), In Proceedings of the 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO), 2019.
Bibtex Entry:
@InProceedings{Brand.2019.Generic,
AUTHOR = {Brand, Thomas and Giese, Holger},
TITLE = {{Generic adaptive monitoring based on executed architecture runtime model queries and events}},
YEAR = {2019},
BOOKTITLE = {Proceedings of the 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)},
URL = {https://doi.org/10.1109/SASO.2019.00012},
ABSTRACT = {Monitoring is a key functionality for automated decision making as it is performed by self-adaptive systems, too. Effective monitoring provides the relevant information on time. This can be achieved with exhaustive monitoring causing a high overhead consumption of economical and ecological resources. In contrast, our generic adaptive monitoring approach supports effectiveness with increased efficiency. Also, it adapts to changes regarding the information demand and the monitored system without additional configuration and software implementation effort. The approach observes the executions of runtime model queries and processes change events to determine the currently required monitoring configuration. In this paper we explicate different possibilities to use the approach and evaluate their characteristics regarding the phenomenon detection time and the monitoring effort. Our approach allows balancing between those two characteristics. This makes it an interesting option for the monitoring function of self-adaptive systems because for them usually very short-lived phenomena are not relevant.}
}
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