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 article, we propose a novel high-reliability resource sched...

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Vydané v:IEEE internet of things journal Ročník 12; číslo 22; s. 48242 - 48261
Hlavní autori: Wang, Weian, Zhu, Xiaorong
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 15.11.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Artificial intelligence
Augmented reality
Cloud computing
Coding distributed computing
Collaboration
Computation offloading
Computational modeling
computing network
Digital twins
Distributed computing
Distributed processing
Energy consumption
Graph theory
Graphs
hypergraph neural network (HGNN)
hypernetwork
Internet of Things
Job shop scheduling
Network latency
Neural networks
Optimization
particle swarm algorithm
Particle swarm optimization
Processor scheduling
Reliability
Resource allocation
Resource management
Resource scheduling
task offloading (TO)
ISSN:2327-4662, 2327-4662
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Shrnutí: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 article, 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 (TO), as well as computing, communication, and caching resources in cloud-edge-end computing networks, we propose the collaborative TO and heterogeneous 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 HGNNs 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.
Bibliografia:ObjectType-Article-1
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ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2025.3604217