Issue No. 08 - Aug. (2012 vol. 24)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2011.88
Donghun Lee , SAP R&D Center Korea, Seoul
Sang K. Cha , Seoul National University, Seoul
Arthur H. Lee , Claremont McKenna College, Claremont
Detecting performance anomalies and finding their root causes are tedious tasks requiring much manual work. Functionality enhancements in DBMS development as in most software development often introduce performance problems in addition to bugs. To detect the problems as soon as they are introduced, which often happens during the early phases of a development cycle, we adopt performance regression testing early in the process. In this paper, we describe a framework that we developed to manage performance anomalies after establishing a set of conditions for a problem to be considered an anomaly. The framework uses Statistical Process Control (SPC) charts to detect performance anomalies and differential profiling to identify their root causes. By automating the tasks within the framework we were able to remove most of the manual overhead in detecting anomalies and reduce the analysis time for identifying the root causes by about 90 percent in most cases. The tools developed and deployed based on the framework allow us continuous, automated daily monitoring of performance in addition to the usual functionality monitoring in our DBMS development.
CUSUM chart, differential profiling, statistical process control (SPC), performance anomaly, DBMS.
S. K. Cha, A. H. Lee and D. Lee, "A Performance Anomaly Detection and Analysis Framework for DBMS Development," in IEEE Transactions on Knowledge & Data Engineering, vol. 24, no. , pp. 1345-1360, 2011.