Task offloading delay minimization in vehicular edge computing based on vehicle trajectory prediction^☆

Abstract

In task offloading, the movement of vehicles causes the switching of connected RSUs and servers, which may lead to task offloading failure or high service delay. In this paper, we analyze the impact of vehicle movements on task offloading and reveal that data preparation time for task execution can be minimized via forward-looking scheduling. Then, a Bi-LSTM-based model is proposed to predict the trajectories of vehicles. The service area is divided into several equal-sized grids. If the actual position of the vehicle and the predicted position by the model belong to the same grid, the prediction is considered correct, thereby reducing the difficulty of vehicle trajectory prediction. Moreover, we propose a scheduling strategy for delay optimization based on the vehicle trajectory prediction. Considering the inevitable prediction error, we take some edge servers around the predicted area as candidate execution servers and the data required for task execution are backed up to these candidate servers, thereby reducing the impact of prediction deviations on task offloading and converting the modest increase of resource overheads into delay reduction in task offloading. Simulation results show that, compared with other classical schemes, the proposed strategy has lower average task offloading delays.

Keywords

Vehicular edge computing / Task offloading / Vehicle trajectory prediction / Delay minimization / Bi-LSTM model

CRediT authorship contribution statement

Feng Zeng: Writing - review & editing, Supervision, Formal analysis, Data curation, Conceptualization. Zheng Zhang: Writing - original draft, Validation, Methodology, Investigation, Data curation. Jinsong Wu: Writing - review & editing, Software, Resources, Methodology.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This work is supported in part by the National Science Foundation of China (Grant No. 62172450), the Key R&D Plan of Hunan Province (Grant No. 2022GK2008) and the Nature Science Foundation of Hunan Province (Grant No. 2020JJ4756). The authors would like to thank the anonymous reviewers for their constructive comments.

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