Research published in Advances in Computer and Communication establishes frameworks for integrating Role-Based Access Control and Attribute-Based Access Control within large-scale distributed architectures, addressing critical limitations in traditional access control through a three-layer decision engine design while achieving 99.999% service availability across mission-critical enterprise platforms.
-- As computing platforms evolved toward cloud computing and the Internet of Things, distributed systems faced challenges managing access control across variable user roles and dynamic authorization demands. Traditional RBAC models cannot meet fine-grained security requirements, while ABAC introduces high policy complexity. This research addresses these challenges through systematic integration combining RBAC's structural clarity with ABAC's contextual flexibility.
The study implements a hybrid architecture across multi-site Content Delivery Network (CDN) systems, analyzing concurrent requests from three regional nodes. Experimental validation demonstrated 65% reduction in authorization response time to 38 milliseconds, 86% cache hit rates, and 94% contextual accuracy. Three-layer architecture comprising edge authorization, local decision, and central coordination enabled distributed deployment with policy snapshots maintaining consistency. Testing with 1,000 concurrent users confirmed effective cross-domain access handling with policy synchronization below 300 milliseconds.
Methodologically, the research develops a decision engine incorporating a four-stage pipeline: Identity Parser, Role Matcher, Attribute Evaluator, and Policy Matcher. Multi-source attribute management integrates heterogeneous data from user directories, device monitors, and environmental variables through a unified interface. Implementation employs a dual-buffer mechanism ensuring non-disruptive policy updates.
Contributing to this research is Yizhou Meng, a software development engineer specializing in distributed systems architecture, secure access control, and cloud infrastructure scalability. Academic credentials include a Master of Science in Electrical and Computer Engineering from Northeastern University. Professional expertise spans enterprise-scale system design, secure coding practices, and high-availability infrastructure across Microsoft, Discover Financial Services, Amazon Web Services, and Fidelity National Information Services.
Yizhou Meng’s current engineering contributions at Microsoft include developing a state machine workflow engine and an RBAC system for Microsoft 365 service authorization, achieving high availability through C# and .NET implementation. Previous accomplishments span secure payment infrastructure development, cloud provisioning workflows, RESTful API optimization serving large user bases, and database performance improvements through multi-threading. Technical competencies encompass software architecture, cloud solutions, and back-end services development using Java, C#, Python, and .NET frameworks.
The integration of hybrid access control research with enterprise-scale infrastructure engineering demonstrates how theoretical frameworks translate into production systems serving millions of users. By establishing scalable authorization mechanisms while implementing secure, high-performance features across payment networks and cloud platforms, this work bridges academic research with practical security solutions, delivering improvements in system reliability, authorization accuracy, and operational efficiency for large-scale distributed architectures.
Contact Info:
Name: Yizhou Meng
Email: Send Email
Organization: Yizhou Meng
Website: https://scholar.google.com/citations?user=QzTekvgAAAAJ&hl=en&oi=ao
Release ID: 89182205
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