Bhattacharyya, A. and Sotiriadis, Stelios and Amza, C. (2017) Online phase detection and characterization of cloud applications. In: UNSPECIFIED (ed.) Cloud Computing Technology and Science (CloudCom), 2017 IEEE International Conference on. IEEE Computer Society, pp. 98-105. ISBN 9781538606933.
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Abstract
In this paper, we introduce a new methodology for automatic phase detection and characterization for applications running on the cloud. In contrast to existing approaches, our approach is novel in the fact that it is non-intrusive, more general (supports multiple programming languages), lightweight and can detect phase changes online as the application runs. We evaluate our approach for a number of C, C++ and Java application servers that are widely used in the cloud. Our method achieves a phase change detection accuracy upto 98.2% with an average detection delay of less than 0.01 seconds after the start or end of a phase. We also show a sample use case of our phase detection and characterization method for anomaly detection in the cloud.
Metadata
| Item Type: | Book Section |
|---|---|
| Additional Information: | Electronic ISSN: 2330-2186. |
| Keyword(s) / Subject(s): | C++ language. Java, cloud computing, C application servers, C++ application servers, Java application servers, anomaly detection, automatic phase detection, average detection delay, characterization method, cloud applications, multiple programming languages, online phase detection, phase change detection accuracy, Anomaly detection, Cloud computing, Hardware, Phase detection, Predictive models, Principal component analysis, Servers, Anomaly Detection, Compiler Analysis, Deep Learning |
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Research Centres and Institutes: | Birkbeck Knowledge Lab |
| Depositing User: | Stelios Sotiriadis |
| Date Deposited: | 01 May 2018 15:45 |
| Last Modified: | 09 Aug 2023 12:43 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/21740 |
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