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Automatic detection of performance deviations in the load testing of large scale systems (2013)
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| Venue: | In Proceedings of International Conference on Software Engineering - Software Engineering in Practice Track, ICSE 2013 |
| Citations: | 9 - 3 self |
BibTeX
@INPROCEEDINGS{Malik13automaticdetection,
author = {Haroon Malik and Hadi Hemmati and Ahmed E. Hassan},
title = {Automatic detection of performance deviations in the load testing of large scale systems},
booktitle = {In Proceedings of International Conference on Software Engineering - Software Engineering in Practice Track, ICSE 2013},
year = {2013}
}
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Abstract
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. Index Terms—Performance, Signature, Machine Learning. I.
Keyphrases
performance deviation large scale system performance counter automatic detection load testing performance analyst unsupervised approach cpu utilization network traffic logistic regression model compare load test deviation prediction search-based technique large scale industrial system case study root-cause analysis detected deviation memory consumption abstract load testing important performance counter service level agreement manageable set run-time system property wrapper-based supervised approach sla violation open source benchmark application semi-automated training phase false positive load test data machine learning supervised approach index term performance load test average precision case study show
