The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Statistical techniques for online anomaly detection in data centers
Abstract
Online anomaly detection is an important step in data center management, requiring light-weight techniques that provide sufficient accuracy for subsequent diagnosis and management actions. This paper presents statistical techniques based on the Tukey and Relative Entropy statistics, and applies them to data collected from a production environment and to data captured from a testbed for multi-tier web applications running on server class machines. The proposed techniques are lightweight and improve over standard Gaussian assumptions in terms of performance.
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Daubechies, I. (1992) · doi:10.1137/1.9781611970104 · Cited by 15,979
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Gupta, M., Abdelsalam, M., Khorsandroo, S., & Mittal, S. (2020) · IEEE Access · doi:10.1109/access.2020.2975142 · Cited by 609
Shirazi, S. N., Gouglidis, A., Farshad, A., & Hutchison, D. (2017) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2017.2760478 · Cited by 284
Xing, S., Wang, Y., & Liu, W. (2025) · Sensors · doi:10.3390/s25113396 · Cited by 23
Metadata
- Authors
- Wang, C., Viswanathan, K., Choudur, L., Talwar, V., Satterfield, W., & Schwan, K.
- Venue
- 12th IFIP/IEEE International Symposium on Integrated Network Management (IM 2011) and Workshops
- Publisher
- IEEE
- Published
- 2011-05
- Pages
- 385-392
- OpenAlex
- W2123967542
- Cited by
- 178
- References
- 1
- FWCI
- 6.86
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Statistical techniques for online anomaly detection in data centers. https://app.cochat.ai/papers/10.1109/inm.2011.5990537
- The paper
- Wang, C., Viswanathan, K., Choudur, L., Talwar, V., Satterfield, W., & Schwan, K. (2011). Statistical techniques for online anomaly detection in data centers. 12th IFIP/IEEE International Symposium on Integrated Network Management (IM 2011) and Workshops. https://doi.org/10.1109/inm.2011.5990537
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
