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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Li, Qicheng Mei, Lijun Li, Shaochun Rong, Liu Chen, Weiye Wang, Fenfei |
| Copyright Year | 2015 |
| Abstract | Application Maintenance Services (AMS) is essential for applications executed on servers to function properly. Its objective is to reduce the application incidents happened and quickly recover services from application failures/issues. The application incidents defined as events when there are some application failures/issues happened are major concerns of AMS, therefore we propose a second order-based anomaly detection method to describe and predict application incidents based on analysis of monitored server traffic metrics. The proposed method first detects anomalies for each metric, second builds the linkage between detected anomalies for all metrics of the server and application incidents, and then predicts potential application incidents. Through the experiments, we find that the presented method provides satisfactory results for identify application incident, which gives more than 90 percentage recall rate while about 65 percentage precision rate. |
| Starting Page | 37 |
| Ending Page | 44 |
| File Size | 410087 |
| Page Count | 8 |
| File Format | |
| ISSN | 21653836 |
| e-ISBN | 9781479999477 |
| DOI | 10.1109/ICSS.2015.23 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-05-08 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Measurement Application Maintenance Services Eal time Anomaly Detection Time series analysis Incident Prediction Maintenance engineering Data models Servers Data mining Monitoring |
| Content Type | Text |
| Resource Type | Article |
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