Multitask Computation Offloading Based on Evolutionary Multiobjective Optimization in Industrial Internet of Things

The development of Industrial Internet of Things (IIoT) has completely changed traditional manufacturing industry. Industrial equipments with limited resources often cannot meet the diverse demands of numerous computing-intensive and latency-sensitive tasks. Mobile edge computing (MEC) offloads thes...

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Bibliographic Details
Published in:IEEE internet of things journal 2024-05, Vol.11 (9), p.15894-15908
Main Authors: Chai, Zheng-Yi, Zhao, Ying-Jie, Li, Ya-Lun
Format: Article
Language:English
Subjects:
Bandwidths
Computation offloading
Computational modeling
Edge computing
Energy consumption
Evolutionary algorithms
Evolutionary computation
Industrial applications
Industrial Internet of Things
Industrial Internet of Things (IIoT)
Internet of Things
Mobile computing
mobile edge computing (MEC)
multiobjective evolutionary algorithm
Multiple objective analysis
multitask offloading
Network latency
Optimization
Servers
Task analysis
Workflow
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Summary:The development of Industrial Internet of Things (IIoT) has completely changed traditional manufacturing industry. Industrial equipments with limited resources often cannot meet the diverse demands of numerous computing-intensive and latency-sensitive tasks. Mobile edge computing (MEC) offloads these tasks to nearby edge servers to achieve lower latency and energy consumption. However, considering the channel interferences of the network and the diverse demands of different tasks, coordinating computation offloading among multiple devices is challenging. To address this challenge, the computation offloading is formulated as a multiobjective optimization problem, and a new task model composed of scientific workflow tasks and concurrency workflow tasks is proposed to represent the multitask in the industrial environment. In addition, a two-hierarchical optimization framework is devised to optimize the bandwidth allocation and the multitask computation offloading through the dynamic bandwidth preallocation and the improved multiobjective evolutionary algorithm based on decomposition with two performance enhancing schemes. Comprehensive experiments demonstrate that the effectiveness and efficiency of our proposed framework in terms of the tradeoffs between latency and energy consumption, as well as the convergence and diversity of obtained nondominated solutions.
ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2024.3349609