Optimized Cloud-Edge Task Allocation and Load Balancing Strategy

The rapid evolution of distributed systems has necessitated innovative approaches for managing task deployment and achieving load balancing in cloudedge environments. This paper addresses the prevalent issue of data overload at cloud computing centers and edge nodes by introducing a Joint Cloud-Edge Task Distribution (JCETD) model. Utilizing pruning algorithms and deep reinforcement learning, the proposed approach ensures efficient resource utilization and optimized task allocation. Key contributions include minimizing computational complexity and enhancing system performance through strategic pruning and adaptive learning techniques.