首页|Multilevel and Energy-Efficient Partial Computation Offloading in Heterogeneous Edge Intelligence

Multilevel and Energy-Efficient Partial Computation Offloading in Heterogeneous Edge Intelligence

扫码查看
原文链接
  • NETL
  • NSTL
  • IEEE
Due to the diversity of edge devices (EDs) and applications, edge systems are heterogeneous and have been applied in artificial intelligence fields, such as smart factories and intelligent transportation, which is called heterogeneous edge intelligence. Many studies employ computation offloading to transfer processing data from resource-scarce EDs to resource-rich edge servers. These studies primarily focus on the overall resource consumption of homogeneous edge systems, neglecting the system heterogeneity and the details of resource consumption. In this article, we construct a system model from a parallel perspective for the heterogeneous edge system with different processors, memory, and applications, which perceives the cost of energy and delay from three levels: system, application, and component. A hybrid metaheuristic algorithm combined with a greedy rule, hybrid mutation, and whale optimization algorithm (GHMWOA) is proposed to realize partial computation offloading. A partial offloading architecture of heterogeneous edge intelligence is proposed to validate our model and algorithm with real-world hardware and software. Experiment results not only show GHMWOA outperforms multiple classical optimization algorithms in minimizing energy consumption, but also discover on which system component energy consumption depends, and how properties of application and system influence the cost of energy.

DelaysEnergy consumptionProgram processorsOptimizationEdge computingComputational modelingCostsArtificial intelligenceServersHeuristic algorithms

Baoyu Xu、Yancheng Ruan、Chenghu Qiu、Shuibing He、Feng Shu、Xiaoyang Kang、Lihua Zhang

展开 >

Academy for Engineering and Technology, Fudan University, Shanghai, China|School of Computer Engineering and Science, Shanghai University, Shanghai, China

School of Computer Engineering and Science, Shanghai University, Shanghai, China

College of Computer Science and Technology, Zhejiang University, Hangzhou, China

School of Electrical and Information Engineering, University of Sydney, Sydney, NSW, Australia

Laboratory for Neural Interface and Brain Computer Interface, MOE Frontiers Center for Brain Science, Academy for Engineering and Technology, Fudan University, Shanghai, China

Academy for Engineering and Technology, Fudan University, Shanghai, China

展开 >

2025

10.1109/JIOT.2025.3529185

IEEE internet of things journal

IEEE internet of things journal

ISSN：
年,卷(期)：2025.12(11)
  • 43