From Edge To Cloud: A Comprehensive Examination Of Hybrid AI Architectures For Mobile Computing Applications

Abstract

This paper presents a comprehensive examination of hybrid AI architectures for mobile computing applications, focusing on the transition from edge to cloud computing environments. The study investigates the performance attributes of edge and cloud computing environments, including latency, security, reliability, and real-time responsiveness, to provide insights into their efficacy in supporting mobile computing applications. The research methodology involves the generation of simulated data to emulate real-world scenarios and the utilization of graphical representations to facilitate comparative analysis across key metrics. Results reveal distinct performance characteristics between edge and cloud computing environments, highlighting the trade-offs and implications for mobile computing applications. Edge computing demonstrates advantages in terms of lower latency, energy efficiency, and real-time responsiveness, making it suitable for applications requiring rapid response times. In contrast, cloud computing offers scalability and reliability, albeit at the cost of higher latency and energy consumption. The findings underscore the importance of considering various performance metrics when designing and deploying hybrid AI architectures for mobile computing applications, with implications for optimizing resource utilization and enhancing user experience. This study contributes to a comprehensive understanding of the evolving landscape of hybrid AI architectures for mobile computing applications, informing future research and development in this domain.