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Task offloading for Industrial Internet of Things based on evolutionary multiobjective multitasking optimization
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DOI：10.1016/j.swevo.2024.101786.png)
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Abstract
En 中文
In mobile edge computing(MEC) scenarios, offloading computation-intensive and delay-critical tasks to nearby mobile edge servers with abundant computing resources can reduce the time delay(TD) of tasks and energy consumption(EC) of terminal devices, thus improving application performance and user experience. In order to reduce the TD of tasks and the EC of factory terminal devices(FTDs), MEC is introduced into the Industrial Internet of Things(IIoT). Because TD and EC are conflicting objectives, most existing research models the task offloading problem(TOP) as a multiobjective optimization problem to optimize both TD and EC. However, as the number of FTDs and tasks in the MEC network increases, the scale and computational cost of the TOP increases, and it is very challenging to obtain the optimal task offloading scheme through multiobjective optimization. Multitasking optimization can make use of knowledge transfer between related tasks to promote the solving efficiency of each task. Based on this, we model the TOP in IIoT as a multiobjective TOP (MTOP). A cheap task that is highly similar to MTOP is constructed, and a novel evolutionary multiobjective multitasking framework is developed, which uses the positive knowledge transfer between tasks to optimize TD and EC in the network. Then, an effective evolutionary multiobjective multitasking algorithm is proposed, which includes multi-functional knowledge transfer strategy(MFKT) and adaptive cheap task update strategy(ACTU). MFKT uses decision variables with different functions in the cheap tasks for positive knowledge transfer, so as to improve the performance of MTOP. ACTU dynamically updates the cheap task to maintain knowledge transfer between tasks. Indifferent test instances, the proposed algorithm is compared with the existing multiobjective and multitasking algorithms. The experimental results show that the proposed algorithm is more competitive in terms of TD and EC, it can guarantee the service quality of the IIoT system and has strong robustness and scalability.
Keywords:
Mobile edge computing
Industrial internet of things
Multiobjective optimization
Task offloading
Evolutionary multiobjective multitasking
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