Task Offloading in Edge Computing: An Evolutionary Algorithm With Multimodel Online Prediction

• Published in: IEEE internet of things journal 2025-02, Vol.12 (3), p.2347-2358
• Main Authors: Nie, Ying, Chai, Zheng-Yi, Lu, Li, Li, Ya-Lun
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Computation offloading; Computational modeling; Convolutional neural networks; Delays; Edge computing; Energy consumption; Evolutionary algorithm (EA); Evolutionary algorithms; Genetic algorithms; hybrid neural network; Internet of Things; Mathematical models; Network latency; Optimization; prediction; Prediction algorithms; Predictive models; Real time; resource allocation; task offloading; Taskload; Training
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2024.3459019

With the rapid development of Internet of Things (IoT) technology, the number of IoT devices has increased dramatically and a large amount of data has been generated. In order to further reduce the resource cost required for task offloading, it is necessary to design task offloading methods with high-energy efficiency and low latency. Considering the correlation between task offloading process and time in real-time interactive scenarios, we propose an evolutionary algorithm (EA) framework with online load prediction based on CNN-GRU hybrid model and channel attention mechanism (AM). In the model construction stage, we first combined convolutional neural network (CNN) and gated recurrent unit (GRU) to learn the features and patterns of historical data. In order to reduce the loss of historical information, the channel AM is introduced into the CNN-GRU model to enhance the influence of important features between information. In the model training stage, the optimal individual training model generated by the EA is used to further optimize the training accuracy and training effect of CNN-GRU-AM. In the test phase, the optimized CNN-GRU-AM network is used to predict the task load online and dynamically allocate computing resources while training the model online iteratively, which further reduces the delay and energy consumption of the task and improves the offloading performance of the system. The simulation results show that the proposed algorithm effectively reduces the system delay and the overall energy consumption.