Performance Anomaly Detection through Sequence Alignment of System-Level Traces

Identifying and diagnosing performance anomalies is essential for maintaining software quality, yet it can be a complex and time-consuming task. Low level kernel events have been used as an excellent data source to monitor performance, but raw trace data is often too large to easily conduct effective analyses. To address this shortcoming, in this paper, we propose a framework for uncovering performance problems using execution critical path data. A critical path is the longest execution sequence without wait delays, and it can provide valuable insight into a program's internal and external dependencies. Upon extracting this data, course grained anomaly detection techniques are employed to determine if a finer grained analysis is required. If this is the case, the critical paths of individual executions are grouped together with machine learning clustering to identify different execution types, and outlying anomalies are identified using performance indicators. Finally, multiple sequence alignment is used to pinpoint specific abnormalities in the identified anomalous executions, allowing for improved application performance diagnosis and overall program comprehension.