Future-Proofing Computer Behavior: A Framework for Predictive Analytics and Seamless Device Integration
Michael Arthur Mills, Tala Talaei Khoei · 2024
This study presents a novel framework designed to facilitate seamless communication between devices across different networks, with a specific focus on cloud computing applications. Our method addresses the challenge of efficient file allocation across devices by integrating advanced networking solutions and deep learning techniques to predict key performance metrics such as Wi-Fi upload and download speeds, GPU usage, CPU usage, and RAM usage with an impressive accuracy of $\mathbf{0. 2}$ Mbps. These predictive capabilities enable informed decisions for resource allocation and task scheduling. At the core of our framework is an innovative relay server strategy that ensures secure and uninterrupted communication between devices, effectively overcoming the hurdles posed by secure networks. This approach not only optimizes resource utilization but also significantly enhances processing speeds, reducing task completion times by up to $\mathbf{3 0 \%}$ in practical scenarios. Our results demonstrate that the framework enhances system performance while maintaining stringent privacy and security standards through its secure relay mechanism. In conclusion, this framework showcases the potential for more efficient and resilient computer networking solutions, offering substantial improvements in resource management and processing efficiency. Our findings provide a foundation for further research to enhance scalability and reduce latency, paving the way for future advancements in seamless device communication across networks.