Reliable Multidimensional Resource Scheduling for Heterogeneous Computing Networks via Coded Distributed Computing and Hypergraph Neural Networks

The emergence of 6G applications such as artificial intelligence, augmented reality, and digital twins has imposed stringent requirements on the high reliability, low latency, and energy efficiency of computing networks. Therefore, in this paper, we propose a novel high-reliability resource scheduling optimization method for heterogeneous computing networks, leveraging hypergraph neural networks (HGNNs) and coded distributed computing (CDC). We first construct a multidimensional resource representation model for computing networks based on hyper-networks, effectively illustrating heterogeneous nodes and their interactions within computing networks. Then, targeting the need for collaborative optimization of task offloading, as well as computing, communication, and caching resources in cloud-edge-end computing networks, we propose the collaborative task offloading and resource allocation (CTOHRA) problem, which minimizes the total task processing delay. By incorporating CDC, we enhance resilience against edge node failures and unstable network links. To solve this problem, we utilize hypergraph neural networks to capture high-order correlations and improve the accuracy of dynamic resource scheduling, and combine particle swarm optimization (PSO) to handle discrete variables and find the global optimal solution. Extensive simulations show that the proposed method can significantly improve the task success rate, reduce the average system latency, and minimize energy consumption, especially under unfavorable network conditions.