AI Power anamoly detection for serveless cost goverance across multi – cloud environments
DOI:
https://doi.org/10.22399/ijcesen.4610Keywords:
AI anomaly detection, serverless computing, cost governance, multi-cloud environments, LSTM model, machine learningAbstract
This study was focused on how well a particular AI-powered anomaly detection tool works. More specifically, this study focused on how well the tool works to improve cost governance for multi-cloud environments. In the study, the AWS Cloud, Microsoft Azure, and Google Cloud hosting environments were examined. One billing pattern change that was recorded was that when the clients chose to use serverless billing, the billing amount increased and the clients were unable to surmise why. One of the most significant results the study identified was how well the LSTM model was able to track and figure out cost misconfigurations, invocation misconfigurations, and cost spike anomalies, while providing few to no false anomaly results. The general cost behaviors displayed were dependent on the monitoring adaptation method, and the study emphasized that intelligent monitoring was necessary to improve adaptability. The research positively concluded that AI-powered anomaly detection systems in multi-cloud environments provided a cost effective and scalable tool that improved organizations' governance and control over their serverless computing costs.
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