Optimizing Cloud-Native App Deployment using Kubernetes Scheduler on AWS Cloud

Deshpande, Swapnil (2023) Optimizing Cloud-Native App Deployment using Kubernetes Scheduler on AWS Cloud. Masters thesis, Dublin, National College of Ireland.

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Abstract

The demand for effective orchestration technologies with smart scheduling algorithms containerization gain traction. This paper presents a novel approach to for scheduling of microservice application and also their management using a popular software containerization platform .The suggested approach, known as the Dependency-Based Scheduler, is centered on ensuring that the interconnected components of a program, known as microservices, function well together within same node. This is crucial because improper handling of these connections by current techniques could result in a less effective use of computer resources and high network bandwidth usage. The new method seeks to decrease the amount of data that is shared between these microservices and speed up overall performance and ultimately help organization to run their microservices efficiently. By doing this, it enables developers and companies to make better use of their resources, resulting in cost savings and improved system performance. This work helps improve Kubernetes’ efficiency on cloud platforms, such as AWS. The demonstrated scheduler’s quality is assessed by comparing its results with that of the default scheduler provided by kubernetes, which simply considers the network usage for communication between different microservices. In the dynamic world of cloud computing, this creative solution addresses the increasing complexity of microservices architectures and advances container orchestration platforms like Kubernetes. It also fits in with the changing landscape of cloud-native technologies.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Makki, Ahmed 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:	26 Mar 2025 16:33
Last Modified:	26 Mar 2025 16:33
URI:	https://norma.ncirl.ie/id/eprint/7339

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