This report examines Artificial Intelligence for IT Operations, commonly known as AIOps, delving deeper into the area of anomaly detection and also investigating the effects of the shift in working methods when a company starts using AI-driven tools. Two anomaly detection machine learning algorithms were explored, Isolation Forest(IF)and Local Outlier Factor(LOF), and compared by testing with a focuson throughput and resource efficiency, to mirror how they would operate in a real-time cloud environment. From a throughput and efficiency perspective, LOF outperforms IF when using default parameters, making it a more suitable choice for cloud environments where processing speed is critical. The higher throughput of LOF indicates that it can handle a larger volume of log data more quickly, which is essential for real-time anomaly detection in dynamic cloud settings. However, LOF’s higher memory usage suggests that it may be less scalable in memory-constrained environments within the cloud. This could lead to increased costs due to the need for more memory resources. The tests show, however, that tuning the models’ parameters are essential to fit them to different types of data. Through a literature study, it is evident that the integration of AI and automation into routine tasks presents an opportunity for workforce development and operational improvement.Addressing cultural barriers and fostering collaboration across IT teamsare essential for successful adoption and implementation.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:umu-227379 |
| Date | January 2024 |
| Creators | Sandén, Therese |
| Publisher | Umeå universitet, Institutionen för datavetenskap |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | u ; 1499 |
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