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Comparative Study of RL Algorithms for Resource Optimization Scheduling in Kubernetes

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Shukla, Jay (2024) Comparative Study of RL Algorithms for Resource Optimization Scheduling in Kubernetes. Masters thesis, Dublin, National College of Ireland.

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Abstract

Cloud computing has evolved from using monolithic architectures to relying on microservices. As microservices have become more common, managing them has also become more straightforward. Tools like Kubernetes, Docker, and OpenShift make it easier to deploy applications in containers, which helps reduce costs and save resources compared to older, monolithic systems. However, one challenge with microservices is that auto-scaling methods often treat each service individually, without considering how they interact with each other. This can lead to inefficient scaling, where either too many resources are used or not enough, potentially harming application performance. This paper suggests a new approach by using Reinforcement Learning (RL) algorithms alongside Kubernetes’ Horizontal Pod Autoscaler (HPA) to improve how resources are managed and scaled. By doing so, we can better optimize performance and resource use in complex, dynamic microservice environments, ultimately improving application efficiency and reducing costs.

Item Type: Thesis (Masters)
Supervisors:
Name
Email
Gupta, Punit
UNSPECIFIED
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Cloud computing
Q Science > QA Mathematics > Algebra > Algorithms > Computer algorithms
Divisions: School of Computing > Master of Science in Cloud Computing
Depositing User: Ciara O'Brien
Date Deposited: 04 Jul 2025 11:04
Last Modified: 04 Jul 2025 11:04
URI: https://norma.ncirl.ie/id/eprint/8056

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