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2015 IEEE 26th International Symposium on Software Reliability Engineering (ISSRE) (2015)
Gaithersbury, MD, USA
Nov. 2, 2015 to Nov. 5, 2015
ISBN: 978-1-5090-0405-8
pp: 24-34
Mostafa Farshchi , School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne, Australia
Jean-Guy Schneider , School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne, Australia
Ingo Weber , Software Systems Research Group, NICTA, Sydney, Australia
John Grundy , School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne, Australia
ABSTRACT
Failure of application operations is one of the main causes of system-wide outages in cloud environments. This particularly applies to DevOps operations, such as backup, redeployment, upgrade, customized scaling, and migration that are exposed to frequent interference from other concurrent operations, configuration changes, and resources failure. However, current practices fail to provide a reliable assurance of correct execution of these kinds of operations. In this paper, we present an approach to address this problem that adopts a regression-based analysis technique to find the correlation between an operation's activity logs and the operation activity's effect on cloud resources. The correlation model is then used to derive assertion specifications, which can be used for runtime verification of running operations and their impact on resources. We evaluated our proposed approach on Amazon EC2 with 22 rounds of rolling upgrade operations while other types of operations were running and random faults were injected. Our experiment shows that our approach successfully managed to raise alarms for 115 random injected faults, with a precision of 92.3%.
INDEX TERMS
log analysis, Cloud application operations, DevOps, Cloud monitoring, anomaly detection, error detection
CITATION

M. Farshchi, J. Schneider, I. Weber and J. Grundy, "Experience report: Anomaly detection of cloud application operations using log and cloud metric correlation analysis," 2015 IEEE 26th International Symposium on Software Reliability Engineering (ISSRE), Gaithersbury, MD, USA, 2015, pp. 24-34.
doi:10.1109/ISSRE.2015.7381796
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