Anomaly detection with Prometheus
Martínez Baselga, Diego (2020)
2020
Degree Programme in Science and Engineering, BSc (Tech) - Degree Programme in Science and Engineering, BSc (Tech)
Tekniikan ja luonnontieteiden tiedekunta - Faculty of Engineering and Natural Sciences
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Hyväksymispäivämäärä
2020-05-14
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202004294645
https://urn.fi/URN:NBN:fi:tuni-202004294645
Tiivistelmä
Prometheus is a widely used application to monitor Kubernetes systems. Nevertheless, it does not provide a suitable solution to detect complex anomalies. This thesis discusses the deployment of a Kubernetes system that uses Kafka. Moreover, a microservice is implemented to generate anomalies and a labeled time-series dataset is generated.
The produced dataset can be used to develop a machine learning algorithm for anomaly detection. In addition, the study explains the tools to understand the dataset and how to use it to develop a plug-in that predicts anomalies and fires Prometheus Alertmanager alarms.
The produced dataset can be used to develop a machine learning algorithm for anomaly detection. In addition, the study explains the tools to understand the dataset and how to use it to develop a plug-in that predicts anomalies and fires Prometheus Alertmanager alarms.
Kokoelmat
- Kandidaatintutkielmat [8929]