|Description:||Effective monitoring of a software system’s runtime behavior is
necessary to evaluate the compliance of performance objectives.
This thesis has emerged in the context of the Kieker framework
addressing application performance monitoring. The contribution
includes a self-adaptive performance monitoring approach allowing
for dynamic adaptation of the monitoring coverage at runtime.
The monitoring data includes performance measures such as
throughput and response time statistics, the utilization of system
resources, as well as the inter- and intra-component control
flow. Based on this data, performance anomaly scores are
computed using time series analysis and clustering methods.
The self-adaptive performance monitoring approach reduces the
business-critical failure diagnosis time, as it saves time-consuming
manual debugging activities. The approach and its underlying
anomaly scores are extensively evaluated in lab experiments.