Is Your Anomaly Detector Ready for Change? Adapting AIOps Solutions to the Real World
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866914748576038912 |
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| author | Poenaru-Olaru, Lorena Karpova, Natalia Cruz, Luis Rellermeyer, Jan van Deursen, Arie |
| author_facet | Poenaru-Olaru, Lorena Karpova, Natalia Cruz, Luis Rellermeyer, Jan van Deursen, Arie |
| contents | Anomaly detection techniques are essential in automating the monitoring of IT systems and operations. These techniques imply that machine learning algorithms are trained on operational data corresponding to a specific period of time and that they are continuously evaluated on newly emerging data. Operational data is constantly changing over time, which affects the performance of deployed anomaly detection models. Therefore, continuous model maintenance is required to preserve the performance of anomaly detectors over time. In this work, we analyze two different anomaly detection model maintenance techniques in terms of the model update frequency, namely blind model retraining and informed model retraining. We further investigate the effects of updating the model by retraining it on all the available data (full-history approach) and only the newest data (sliding window approach). Moreover, we investigate whether a data change monitoring tool is capable of determining when the anomaly detection model needs to be updated through retraining. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_10421 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Is Your Anomaly Detector Ready for Change? Adapting AIOps Solutions to the Real World Poenaru-Olaru, Lorena Karpova, Natalia Cruz, Luis Rellermeyer, Jan van Deursen, Arie Machine Learning Software Engineering Anomaly detection techniques are essential in automating the monitoring of IT systems and operations. These techniques imply that machine learning algorithms are trained on operational data corresponding to a specific period of time and that they are continuously evaluated on newly emerging data. Operational data is constantly changing over time, which affects the performance of deployed anomaly detection models. Therefore, continuous model maintenance is required to preserve the performance of anomaly detectors over time. In this work, we analyze two different anomaly detection model maintenance techniques in terms of the model update frequency, namely blind model retraining and informed model retraining. We further investigate the effects of updating the model by retraining it on all the available data (full-history approach) and only the newest data (sliding window approach). Moreover, we investigate whether a data change monitoring tool is capable of determining when the anomaly detection model needs to be updated through retraining. |
| title | Is Your Anomaly Detector Ready for Change? Adapting AIOps Solutions to the Real World |
| topic | Machine Learning Software Engineering |
| url | https://arxiv.org/abs/2311.10421 |