A Cloud-Edge-Devices Computing Power Allocation Algorithm Based on Intelligent Collaborative Computing

Cloud-edge-devices collaborative computing has become an essential paradigm for optimizing resource allocation in modern distributed systems. However, the lack of intelligent and adaptive mechanisms often leads to inefficient utilization of computing resources, resulting in performance bottlenecks and energy wastage. This paper proposes a novel computing power allocation algorithm based on intelligent collaborative computing (ICCA) that integrates cloud, edge, and end devices into a unified framework. The proposed algorithm leverages machine learning techniques to dynamically allocate computing tasks across the hierarchy, considering factors such as task complexity, network latency, device capabilities, and energy consumption. Simulation results demonstrate that the ICCA algorithm achieves significant improvements in task completion time, energy efficiency, and system throughput compared to traditional static allocation methods. Furthermore, the algorithm exhibits robustness under varying workloads and network conditions, making it suitable for real-world applications in smart cities, industrial IoT, and autonomous systems.