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Bachelor's Thesis
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Optimization of Deployment Strategies Using Machine Learning
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Advisor
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Date
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December 15, 2019 - April 15, 2020
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| Abstract | The thesis investigates the improvement of current deployment strategies. We propose a mechanism that learns the behavior of the system within a Kubernetes Cluster by monitoring the system as well as user requests. We propose an intelligent deployment mechanism that can predict the best possible deployment strategy to optimize resource usage. We plan to visualize the relationships within the services of the cluster and the predictions made by the tool. You can find the open source implementation of the workflow and supporting tools at https://github.com/Apodini/pythia |
