The DOI matches its Crossref record; 88 of the 88 references it cites were found in a registry or an index.
A survey of online failure prediction methods
Abstract
With the ever-growing complexity and dynamicity of computer systems, proactive fault management is an effective approach to enhancing availability. Online failure prediction is the key to such techniques. In contrast to classical reliability methods, online failure prediction is based on runtime monitoring and a variety of models and methods that use the current state of a system and, frequently, the past experience as well. This survey describes these methods. To capture the wide spectrum of approaches concerning this area, a taxonomy has been developed, whose different approaches are explained and major concepts are described in detail.
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Teinemaa, I., Dumas, M., Rosa, M. L., & Maggi, F. M. (2019) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3301300 · Cited by 351
Event Prediction in the Big Data Era: A Systematic Survey
Zhao, L. (2022) · ACM Computing Surveys · doi:10.1145/3450287 · Cited by 135
A survey of AIOps methods for failure management
Notaro, P., Cardoso, J., & Gerndt, M. (2021) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3483424 · Cited by 142
Fault prediction and reliability analysis in a real cellular network
Kumar, Y., Farooq, H., & Imran, A. (2017) · 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc.2017.7986437 · Cited by 26
AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges
Cheng, Q., Sahoo, D., Saha, A., Yang, W., Liu, C., Woo, G., Singh, M., Saverese, S., & Hoi, S. C. H. (2023) · arXiv · doi:10.48550/arxiv.2304.04661 · Cited by 23
Metadata
- Authors
- Salfner, F., Lenk, M., & Malek, M.
- Venue
- ACM Computing Surveys
- Published
- 2010-03
- Volume
- 42
- Issue
- 3
- Pages
- 1-42
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W2164463086
- Cited by
- 560
- References
- 88
- FWCI
- 56.94
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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