Title: Cloud-Based Deployment of Knowledge-Based Systems: Architecture and Case Study
Authors: Yousef Ahmed Al-Afifi and Samy S. Abu-Naser
Volume: 9
Issue: 8
Pages: 159-165
Publication Date: 2025/08/28
Abstract:
Knowledge-Based Systems (KBS) have historically served as vital tools in domains requiring expert-level decision-making, including medicine, law, finance, and engineering. Traditional local deployments of KBS, while effective, face notable limitations in scalability, accessibility, maintenance, and infrastructure costs. The advent of cloud computing introduces a paradigm shift, offering elastic scalability, global accessibility, and reduced operational overheads. This paper explores the architectural principles underpinning cloud-based KBS, reviews recent developments in the literature, and presents a detailed case study of deploying a medical diagnosis expert system on the Microsoft Azure cloud platform. Key system components include Azure Virtual Machines, Blob Storage, API Management, and performance monitoring tools. Load testing using Apache JMeter simulated real-world usage scenarios, revealing significant improvements in system responsiveness and operational efficiency. However, challenges remain, particularly around latency and data privacy under regulatory frameworks such as GDPR. The paper contributes a practical framework for the deployment and evaluation of intelligent systems on the cloud and offers a discussion on future trends and optimization strategies for intelligent cloud services.