Optimizing Multi-Cloud Deployment for Microservices Using a Greedy Selection Strategy

Thirukumaran, Ruban (2024) Optimizing Multi-Cloud Deployment for Microservices Using a Greedy Selection Strategy. Masters thesis, Dublin, National College of Ireland.

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Abstract

This project aims solving a microservice placement problem in the multiple clouds through the greedy heuristic algorithm. The primary goal is to minimize the overall deployment cost, latency, and maximize the availability by identifying the right cloud provider out of the available cloud provider list. The field is developed using a greedy selection algorithm created in the Flask framework; the solution is then placed in Docker containers to be tested on AWS as well as on Microsoft Azure. They are the greedy algorithm that must consider cost, latency, and the success rate to determine the best provider for each microservice at run time. Analysis also shows that the algorithm outperforms its baselines in cost, with an average of savings of $20 percent with reasonable latency and availability. These insights show that the algorithm is useful in enhancing multi-cloud utilization hence recommendable for improving cloud resources affordably. Machine learning may be considered in the future work to achieve better solutions, and commercial applications may consider this model for cloud services in industries where high scaling and performance are important.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Heeney, Sean 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
Divisions:	School of Computing > Master of Science in Cloud Computing
Depositing User:	Ciara O'Brien
Date Deposited:	17 Jul 2025 12:35
Last Modified:	17 Jul 2025 12:35
URI:	https://norma.ncirl.ie/id/eprint/8159

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