Title :
Automatic detection of performance deviations in the load testing of Large Scale Systems
Author :
Malik, Haroon ; Hemmati, Hadi ; Hassan, Ahmed E.
Author_Institution :
Software Anal. & Intell. Lab. (SAIL), Queen´s Univ., Kingston, ON, Canada
Abstract :
Load testing is one of the means for evaluating the performance of Large Scale Systems (LSS). At the end of a load test, performance analysts must analyze thousands of performance counters from hundreds of machines under test. These performance counters are measures of run-time system properties such as CPU utilization, Disk I/O, memory consumption, and network traffic. Analysts observe counters to find out if the system is meeting its Service Level Agreements (SLAs). In this paper, we present and evaluate one supervised and three unsupervised approaches to help performance analysts to 1) more effectively compare load tests in order to detect performance deviations which may lead to SLA violations, and 2) to provide them with a smaller and manageable set of important performance counters to assist in root-cause analysis of the detected deviations. Our case study is based on load test data obtained from both a large scale industrial system and an open source benchmark application. The case study shows, that our wrapper-based supervised approach, which uses a search-based technique to find the best subset of performance counters and a logistic regression model for deviation prediction, can provide up to 89% reduction in the set of performance counters while detecting performance deviations with few false positives (i.e., 95% average precision). The study also shows that the supervised approach is more stable and effective than the unsupervised approaches but it has more overhead due to its semi-automated training phase.
Keywords :
input-output programs; program testing; public domain software; regression analysis; software performance evaluation; unsupervised learning; CPU utilization; LSS; SLA violations; automatic performance deviation detection; deviation prediction; disk I-O; large scale systems; load testing; logistic regression model; machine learning; memory consumption; network traffic; open source benchmark application; performance counters; root-cause analysis; run-time system properties; search-based technique; service level agreements; wrapper-based supervised approach; Control charts; Large-scale systems; Logistics; Monitoring; Principal component analysis; Radiation detectors; Testing; Machine Learning; Performance; Signature;
Conference_Titel :
Software Engineering (ICSE), 2013 35th International Conference on
Conference_Location :
San Francisco, CA
Print_ISBN :
978-1-4673-3073-2
DOI :
10.1109/ICSE.2013.6606651