self-adaptive software system monitoring for performance anomaly localization
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.
adaptive monitoring anomaly detection failure diagnosis