A hierarchical optimization approach for industrial task offloading and resource allocation in edge computing systems
With the continuous expansion of the scale of the industrial Internet, edge computing has become an indispensable part of the industrial Internet. In order to quickly handle the massive computation tasks of production devices and monitoring devices in industrial production and ensure the safety and...
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| Veröffentlicht in: | Cluster computing 2024-08, Vol.27 (5), p.5981-5993 |
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| creator | Dong, Jiadong Chen, Lin Zheng, Chunxiang Pan, Kai Guo, Qinghu Wu, Shunfeng Wang, Zhaoxiang |
| description | With the continuous expansion of the scale of the industrial Internet, edge computing has become an indispensable part of the industrial Internet. In order to quickly handle the massive computation tasks of production devices and monitoring devices in industrial production and ensure the safety and efficiency of industrial production. This article considers the Joint Task Offloading and Resource Allocation (JTORA) optimization problem, which is measured by the weighted sum of task completion time and energy consumption, and includes the joint optimization of task offloading decision, uplink power allocation, and computing resource allocation. Also further decompose the JTORA problem into (i) a Resource Allocation (RA) problem with fixed task offloading decision and (ii) a Task Offloading (TO) problem that optimizes the optimal-value function corresponding to the RA problem for hierarchical optimization. This paper adopts Deep Reinforcement Learning (DRL) algorithm to solve the RA problem and a heuristic algorithm for the TO problem. Simulation experimental results show that the proposed JTORA optimization scheme can significantly reduce the production time and device energy consumption in industrial production over traditional approaches. |
| doi_str_mv | 10.1007/s10586-024-04276-y |
| format | Article |
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| subjects | Algorithms Bandwidths Cloud computing Completion time Computation offloading Computer Communication Networks Computer Science Computing time Data analysis Edge computing Efficiency Energy consumption Heuristic methods Industrial production Industry 4.0 Information technology Internet Internet of Things Machine learning Operating Systems Optimization Processor Architectures Resource allocation Resource management Time measurement |
| title | A hierarchical optimization approach for industrial task offloading and resource allocation in edge computing systems |
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