Master Thesis: LLM-based log processing and Anomaly Detection in Cloud Orchestartion Systems
Teknik, data och digitalt · Data, AI och analys · Data engineering · Maskininlärning · Mjukvaruutveckling
I korthet
This Master's thesis will investigate the effectiveness of Large Language Models (LLMs) for log processing and anomaly detection in large-scale cloud orchestration systems, comparing them to traditional log parsing and ML techniques. The work will involve studying research, establishing baselines, designing and implementing both LLM-based and conventional pipelines, and evaluating their performance in detecting anomalies and diagnosing faults.
Ansvarsområden
- Study relevant research on log parsing, anomaly detection, fault diagnosis, and LLM-based analysis of operational data.
- Establish a representative baseline for healthy operation by collecting and analyzing logs from the target system under normal conditions.
- Design and implement one or more conventional log-processing pipelines using log parsing and ML techniques.
- Design and implement an LLM-based approach for log processing and anomaly detection.
- Define, construct, and inject representative fault scenarios into the target environment.
- Evaluate how well the different approaches detect anomalies, identify fault types, and determine probable root causes.
- Analyze the results and assess the practical applicability of LLMs for monitoring large-scale distributed systems.
Krav
- Ongoing Msc studies within Computer Science, Electrical Engineering, Physics or Maths
- Knowledge of LLMs, AI, ML, Cloud
Färdigheter
Artificial IntelligenceAnalysisAlgorithmsAPIEngineeringComputer ScienceAuthenticationAnalyticsAmazon Web Services (AWS)AgileLLMAIMLCloud
#LLM#anomaly detection#cloud orchestration#log processing#machine learning#distributed systems#Kubernetes#OpenStack#thesis#master thesis
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Företag
EricssonPublicerade jobb
för 11 timmar sedan
Anställningstyp
Praktik
Arbetsform
På plats
Erfarenhetsnivå
Student
Platser
Lund, Sweden
Kvalifikation
Masterexamen
Sökande
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