Intelligent Edge-Cloud Integration for Secure and Scalable AI-Driven Computer Systems Optimization

Abstract

The rapid proliferation of artificial intelligence (AI) applications has significantly increased the demand for low-latency, secure, and scalable computing infrastructures. Traditional centralized cloud systems often struggle to meet real-time processing and data privacy requirements, especially in latency-sensitive domains such as smart cities, healthcare, and industrial automation. This research proposes an intelligent edge-cloud integration framework designed to optimize AI-driven computer systems through distributed intelligence, adaptive workload orchestration, and enhanced security mechanisms. The framework leverages edge computing for real-time inference and cloud computing for large-scale model training and coordination, ensuring efficient resource utilization and reduced latency. Additionally, the study incorporates privacy-preserving techniques such as federated learning and secure data transmission protocols to mitigate security risks. Experimental evaluations demonstrate improvements in response time, system scalability, and energy efficiency compared to traditional cloud-only architectures. The results highlight the effectiveness of hybrid edge-cloud ecosystems in enabling robust and secure AI-driven system optimization. This work contributes to advancing next-generation intelligent computing infrastructures capable of handling dynamic workloads in a secure and scalable manner.