Edge-Cloud Hybrid Architecture for Distributed AI Applications

This paper explores an optimized edge-cloud hybrid architecture for distributed artificial intelligence (AI) applications. We introduce a novel framework that dynamically allocates computational tasks between edge devices and cloud resources, addressing the challenges of latency, bandwidth utilization, and energy efficiency. Our proposed architecture employs a decision-making algorithm that considers application requirements, network conditions, and resource availability to optimize workload distribution. Experimental results demonstrate that the hybrid approach outperforms pure edge or cloud-only solutions, reducing response time by up to 47% and energy consumption by 38% while maintaining high accuracy for AI inference tasks. The architecture proves particularly effective for applications requiring real-time processing with limited bandwidth, such as autonomous vehicles, smart cities, and industrial IoT deployments.