Tavva, Rahul (2025) Neural network pathways: Visualizing iRaaS decision intelligence in modern infrastructure. World Journal of Advanced Engineering Technology and Sciences, 15 (3). pp. 1790-1797. ISSN 2582-8266
![WJAETS-2025-1109.pdf [thumbnail of WJAETS-2025-1109.pdf]](https://eprint.scholarsrepository.com/style/images/fileicons/text.png)
WJAETS-2025-1109.pdf - Published Version
Available under License Creative Commons Attribution Non-commercial Share Alike.
Abstract
This article explores Intelligent Routing as a Service (iRaaS), a transformative approach to network routing that leverages machine learning to overcome the limitations of traditional protocols. The article explores how iRaaS employs dynamic adaptability, service orientation, and context awareness to address the challenges of increasingly complex network environments. The discussion first examines core architectural elements, exploring both microservice structures and machine learning pipelines. It then advances to a comparative evaluation of performance metrics, contrasting the system against traditional routing approaches and demonstrating significant enhancements in response time, processing capacity, and system stability. The article further shows security and governance frameworks necessary for iRaaS implementation, including threat models specific to AI-driven routing and compliance considerations. The article concludes by investigating future research directions, particularly integration with intent-based networking, edge computing applications, standardization efforts, and emerging use cases across government, finance, healthcare, and enterprise sectors.
Item Type: | Article |
---|---|
Official URL: | https://doi.org/10.30574/wjaets.2025.15.3.1109 |
Uncontrolled Keywords: | Machine learning routing; Network intelligence; Intent-based networking; Edge computing; Cloud-native infrastructure |
Depositing User: | Editor Engineering Section |
Date Deposited: | 16 Aug 2025 13:16 |
Related URLs: | |
URI: | https://eprint.scholarsrepository.com/id/eprint/4833 |