SK
S. Kutsarov
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Bachelor thesis
(2026)
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S. Kutsarov, S.M.B.S. Samarakoon Mudiyanselage, Nitinder Mohan, Jérémie Decouchant
Modern cloud-native systems generate large amounts of telemetry data, including logs, metrics, and traces, which are useful for monitoring and diagnosing system behavior. However, the effectiveness of machine learning-based anomaly detection varies significantly depending on the telemetry modality and the nature of the faults.
With the ever-increasing demands of 5G applications and upcoming 6G systems, operators must ensure that their networks can rapidly respond to and mitigate faults, with detection being the first step. This project investigates the performance of machine learning models for anomaly detection when logs, metrics, and traces are analyzed independently, with the goal of understanding their relative strengths and limitations.
The study analyzes the performance of fifteen models across five different fault classes, comprising 22 faults in total. After creating an appropriate dataset, each model was evaluated using data collected from several runs. The results show that a single modality cannot detect all faults, two modalities can detect all but one fault, and all three modalities together can detect every fault. ...
With the ever-increasing demands of 5G applications and upcoming 6G systems, operators must ensure that their networks can rapidly respond to and mitigate faults, with detection being the first step. This project investigates the performance of machine learning models for anomaly detection when logs, metrics, and traces are analyzed independently, with the goal of understanding their relative strengths and limitations.
The study analyzes the performance of fifteen models across five different fault classes, comprising 22 faults in total. After creating an appropriate dataset, each model was evaluated using data collected from several runs. The results show that a single modality cannot detect all faults, two modalities can detect all but one fault, and all three modalities together can detect every fault. ...
Modern cloud-native systems generate large amounts of telemetry data, including logs, metrics, and traces, which are useful for monitoring and diagnosing system behavior. However, the effectiveness of machine learning-based anomaly detection varies significantly depending on the telemetry modality and the nature of the faults.
With the ever-increasing demands of 5G applications and upcoming 6G systems, operators must ensure that their networks can rapidly respond to and mitigate faults, with detection being the first step. This project investigates the performance of machine learning models for anomaly detection when logs, metrics, and traces are analyzed independently, with the goal of understanding their relative strengths and limitations.
The study analyzes the performance of fifteen models across five different fault classes, comprising 22 faults in total. After creating an appropriate dataset, each model was evaluated using data collected from several runs. The results show that a single modality cannot detect all faults, two modalities can detect all but one fault, and all three modalities together can detect every fault.
With the ever-increasing demands of 5G applications and upcoming 6G systems, operators must ensure that their networks can rapidly respond to and mitigate faults, with detection being the first step. This project investigates the performance of machine learning models for anomaly detection when logs, metrics, and traces are analyzed independently, with the goal of understanding their relative strengths and limitations.
The study analyzes the performance of fifteen models across five different fault classes, comprising 22 faults in total. After creating an appropriate dataset, each model was evaluated using data collected from several runs. The results show that a single modality cannot detect all faults, two modalities can detect all but one fault, and all three modalities together can detect every fault.