Deep Reinforcement Learning in Optimizing Kubernetes Workload Controllers
Mobile & Cloud Computing Laboratory
This thesis delves into the complex dynamics of cloud computing clusters leveraging container technologies, particularly focusing on the Kubernetes tool and underlined container engine, containerd/Docker. As modern cloud infrastructures gravitate towards container orchestration, Kubernetes and its components, including the cluster autoscaler and workload controller, have gained paramount importance. While the existing system provides a foundational structure, this thesis focuses on the integration of Deep Reinforcement Learning (DRL) to enhance the adaptability and efficiency of Kubernetes workload controllers. By applying DRL, we aim to achieve an intelligent, self-adjusting system that can autonomously optimize container resource allocation and workload distribution, ensuring cost-effective scalability and heightened system performance.
Graduation Theses defence year
Spoken language (s)
Requirements for candidates
Application of contact
Chinmaya Kumar Dehury