Unit:
Κατεύθυνση Τηλεπικοινωνίες και Επεξεργασία ΣήματοςΠληροφορική
Author:
Portokalis Iordanis
Supervisors info:
Χατζηευθυμιάδης Ευστάθιος, Διδακτικό ερευνητικό προσωπικό, Τμήμα Πληροφορικής και Τηλεπικοινωνιών ΕΚΠΑ
Original Title:
Advanced Model for Cloud Resources
Translated title:
Advanced Model for Cloud Resources
Summary:
The rapidly evolving field of container orchestration has seen Kubernetes emerge as a frontrunner, offering robust solutions for managing complex, distributed systems. This thesis introduces a novel approach to Kubernetes optimization by applying principles of graph theory. The research is grounded in the hypothesis that a graph-theoretical perspective can enhance understanding and management of Kubernetes clusters, particularly in the areas of resource allocation, networking, and configuration man-agement. An experimental setup was designed within a controlled Kubernetes envi-ronment to test this hypothesis. Key performance indicators such as CPU and memory utilization, network throughput, and system scalability were measured before and after applying graph-theoretical optimization strategies. The results demonstrated significant improvements in resource efficiency, network performance, and overall system scala-bility, supporting the hypothesis. These findings have implications not only for aca-demic research in cloud computing and distributed systems but also offer practical in-sights for Kubernetes practitioners. This thesis paves the way for further exploration into the integration of theoretical models with cloud-native technologies, potentially re-shaping Kubernetes optimization strategies in both industry and academia.
Main subject category:
Technology - Computer science
Keywords:
Kubernetes, Graph Theory, Container Orchestration, Cloud Computing Optimization, Cpu.
