Improving the scalability of Node.js applications in the cloud by integrating parallel processing and multi-threading, followed by a performance assessment across AWS, GCP cloud platforms

Addula, Rohith (2024) Improving the scalability of Node.js applications in the cloud by integrating parallel processing and multi-threading, followed by a performance assessment across AWS, GCP cloud platforms. Masters thesis, Dublin, National College of Ireland.

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Abstract

The single-threaded, non-blocking asynchronous architecture of Node.js applications made it more suitable for handling I/O operations and concurrent requests efficiently. However, this architecture constraints the Node.js apps by limiting the scalability in handling CPU-intensive workloads. This research aims to address this with a custom load-balancing algorithm designed to handle the task distribution across the available resources by prioritising the tasks based on the resource availability. This approach ensures efficient resource utilisation and workload distribution by optimising the performance and scalability. The proposed load-balancing algorithm is integrated into a micro-service Node.js application with different endpoints enabled to perform the experiments with different workloads.

The application was deployed across Amazon Web Services (AWS) and Google Cloud Platform (GCP) as containerised and non-containerised applications to evaluate the proposed solution. The performance metrics such as CPU utilisation, memory usage and throughput are collected after every experiment with different workloads. The experimental results shows that custom load-balancing algorithm optimised scalability by 35% on average when compared with default load-balancing mechanism in Node.js application. The results shows that in containerised instances AWS outperformed GCP by 18% in handling CPU-intensive tasks showing that AWS is more suitable to handle such tasks. This research also identifies optimal deployment strategies for high performance applications in the cloud environments. This research also demonstrates the capabilities of Node.js applications in high performance computing (HPC)

Item Type:	Thesis (Masters)
Supervisors:	Name Email Arun, Shreyas Setlur 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:	02 Jul 2025 18:23
Last Modified:	02 Jul 2025 18:23
URI:	https://norma.ncirl.ie/id/eprint/8003

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