Optimizing Data Privacy and Threat Detection in Cloud-Edge Collaborative Systems: AI-Driven Approaches with MCC, PSO, and CDNs

Abstract

This study proposes enhancing cloud-edge collaborative systems by applying novel AI techniques focused on mobile cloud computing, particle swarm optimization, and content delivery networks. Such technologies include better optimization of resources, data security, and real-time threats within dynamic computing environments. The approach that this platform uses combines the scalability of mobile cloud computing with the prowess of particle swarm optimization in terms of resource distribution as well as content delivery networks' low-latency capabilities while utilizing artificial intelligence models for secure and real-time data administration. Improve cloud-edge security and efficiency by integrating AI-driven mobile cloud computing, particle swarm optimization, and content delivery networks to maximize resource usage, detect threats in an instant, and provide content without delay. With the help of mobile cloud computing, particle swarm optimization, and content delivery networks, it was able to attain more desirable performance metrics with a 95% accuracy level and 96% recall value, which signifies its feasibility to optimize collaborative capabilities across cloud infrastructures. In short, this AI-based architecture can resolve most of the problems with the present-day distributed systems by providing a potent solution for cloud-edge cooperation, privacy, scalability, and productivity.