Exploiting hardware performance counters with flow and context sensitive profiling
Proceedings of the ACM SIGPLAN 1997 conference on Programming language design and implementation
The Palladio component model for model-driven performance prediction
Journal of Systems and Software
ACM Computing Surveys (CSUR)
Detecting application-level failures in component-based Internet services
IEEE Transactions on Neural Networks
A self-adaptive monitoring framework for component-based software systems
ECSA'11 Proceedings of the 5th European conference on Software architecture
MSEPT'12 Proceedings of the 2012 international conference on Multicore Software Engineering, Performance, and Tools
Performance problem diagnostics by systematic experimentation
Proceedings of the 18th international doctoral symposium on Components and architecture
Automated root cause isolation of performance regressions during software development
Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering
Managing non-functional uncertainty via model-driven adaptivity
Proceedings of the 2013 International Conference on Software Engineering
Supporting swift reaction: automatically uncovering performance problems by systematic experiments
Proceedings of the 2013 International Conference on Software Engineering
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Autonomic computing components and services require continuous monitoring capabilities for collecting and analyzing data of runtime behavior. Particularly for software systems, a trade-off between monitoring coverage and performance overhead is necessary. In this paper, we propose an approach for localizing performance anomalies in software systems employing self-adaptive monitoring. Time series analysis of operation response times, incorporating architectural information about the diagnosed software system, is employed for anomaly localization. Comprising quality of service data, such as response times, resource utilization, and anomaly scores, OCL-based monitoring rules specify the adaptive monitoring coverage. This enables to zoom into a system's or component's internal realization in order to locate root causes of software failures and to prevent failures by early fault determination and correction. The approach has been implemented as part of the Kieker monitoring and analysis framework. The evaluation presented in this paper focuses on monitoring overhead, response time forecasts, and the anomaly detection process.