System identification: theory for the user
System identification: theory for the user
Artificial Intelligence Review - Special issue on lazy learning
The Vision of Autonomic Computing
Computer
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OOPSLA '04 Proceedings of the 19th annual ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
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DSN '06 Proceedings of the International Conference on Dependable Systems and Networks
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Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
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ICAC '06 Proceedings of the 2006 IEEE International Conference on Autonomic Computing
Leveraging many simple statistical models to adaptively monitor software systems
ISPA'07 Proceedings of the 5th international conference on Parallel and Distributed Processing and Applications
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Proceedings of the 2008 international workshop on Software engineering for adaptive and self-managing systems
Adaptive Monitoring with Dynamic Differential Tracing-Based Diagnosis
DSOM '08 Proceedings of the 19th IFIP/IEEE international workshop on Distributed Systems: Operations and Management: Managing Large-Scale Service Deployment
Information-theoretic modeling for tracking the health of complex software systems
CASCON '08 Proceedings of the 2008 conference of the center for advanced studies on collaborative research: meeting of minds
System monitoring with metric-correlation models: problems and solutions
ICAC '09 Proceedings of the 6th international conference on Autonomic computing
Problem classification method to enhance the ITIL incident and problem
IM'09 Proceedings of the 11th IFIP/IEEE international conference on Symposium on Integrated Network Management
Heteroscedastic models to track relationships between management metrics
IM'09 Proceedings of the 11th IFIP/IEEE international conference on Symposium on Integrated Network Management
Leveraging many simple statistical models to adaptively monitor software systems
International Journal of High Performance Computing and Networking
Workload-aware anomaly detection for Web applications
Journal of Systems and Software
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Many runtime metrics can be collected from modern software systems. Stable statistical relationships exist among these metrics. Deviation from these stable relationships indicates potential problems, allowing diagnosis of failures. There exist many modeling techniques to represent these relationships. However, which one to use is a question that has yet to be studied. In this paper we compare the use of simple linear regression (SLR) to some of its more complex variants, including autoregressive regression and locally weighted regression. We consider the component coverage, model robustness, accuracy of diagnosis, and computation cost. Our study finds that while more flexible models can improve diagnosis accuracy, they achieve it at the cost of reduced robust-ness. In particular, we found the autoregressive regression model with exogenous input (ARX) to provide the most accurate diagnosis; however, it is the least robust of the techniques considered and the second most expensive. This study also finds that smoothing and other data transformations can noticeably improve results of SLR, thus providing an efficient alternative to ARX.