Reactive Microservice Architecture-Based Data Connector for Data Sharing Framework

Gan, Sze Kai (2024) Reactive Microservice Architecture-Based Data Connector for Data Sharing Framework. Masters thesis, Tunku Abdul Rahman University of Management and Technology.

Text 14. Gan Sze Kai (MCS).pdf Restricted to Registered users only Download (12MB)

Abstract

The Fourth Industrial Revolution (IR4.0) has revolutionized manufacturing with advanced technologies. However, traditional monolithic architectures pose challenges in scalability, maintenance, and interoperability. This research addresses these issues by proposing a microservices-based approach for the IDS connector within the Industry 4.0 framework. The primary objective is to enhance the design of IDS connector in the International Data Spaces Reference Architecture Model (IDS-RAM) by leveraging microservices' scalability, fault tolerance, and modularity. Additionally, the study explores the evolution from conventional microservices to reactive microservices, illustrating how the integration of reactive streams and clustering enhances system performance and responsiveness. The methodology employed combines comprehensive literature review, experimental evaluation, and in-depth case study analysis. The experimental evaluation involves transforming the IDS-Connector module into a microservices-based ecosystem, demonstrating its scalability with cluster management and the efficiency of using reactive streaming for data processing. The results demonstrate that reactive streaming effectively utilizes limited resources compared to the traditional approach using REST API. Discussion encompasses an analysis of the observed limitations in the proposed solution and potential future enhancements to address these limitations, focusing on optimizing resource usage and further improving system performance and responsiveness. This research contributes to advancing the understanding of microservices' application in IDSRAM, emphasizing the advantages of reactive streams and clustering in enhancing system efficiency and scalability.