A Comparative Study of Metaheuristic Algorithms for Enhancing Topology-Aware Scheduling in Kubernetes

Mall, Harsh Harendra Singh (2023) A Comparative Study of Metaheuristic Algorithms for Enhancing Topology-Aware Scheduling in Kubernetes. Masters thesis, Dublin, National College of Ireland.

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Abstract

Containerization has become a game-changing technology that completely transforms how we package software applications. And when it comes to managing and scaling these containerized apps in the cloud, effective container orchestration becomes absolutely crucial. Kubernetes, a widely recognized platform, excels in orchestrating the deployment, scaling, and management of containers. However, the proposed solution of default scheduling method employed by Kubernetes for allocating containers to suitable nodes within the datacenter is primarily optimized for cloud workloads, , but unfortunately does not consider topological information about distributed environments int the context of fog and edge computing.

In this research, we propose a comparative analysis of three metaheuristic algorithms applied to Kubernetes to achieve topology aware scheduling. Our investigation centers around crucial metrics like execution time and the cost when the topology information is considered when scheduling containers. Experiments show that Genetic Algorithms has the lowest overall execution time and Local Search Algorithm is better cost effective. We observe a latency improvement of Genetic Algorithm as compared to Local Search Algorithm and a cost reduction of Local Search algorithm when compared to Genetic and Tabu Search Algorithms.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Lugones, Diego UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software T Technology > T Technology (General) > Information Technology > Computer software 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:	Tamara Malone
Date Deposited:	09 Oct 2024 17:54
Last Modified:	09 Oct 2024 17:54
URI:	https://norma.ncirl.ie/id/eprint/7089

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