Task-Offloading Strategy Based on Performance Prediction in Vehicular Edge Computing

Feng Zeng, Jiangjunzhe Tang, Chengsheng Liu, Xiaoheng Deng and Wenjia Li
Additional contact information
Feng Zeng: School of Computer Science and Engineering, Central South University, Changsha 410083, China
Jiangjunzhe Tang: School of Computer Science and Engineering, Central South University, Changsha 410083, China
Chengsheng Liu: School of Computer Science and Engineering, Central South University, Changsha 410083, China
Xiaoheng Deng: School of Computer Science and Engineering, Central South University, Changsha 410083, China
Wenjia Li: Department of Computer Science, New York Institute of Technology, New York, NY 10023, USA

Mathematics, 2022, vol. 10, issue 7, 1-19

Abstract: In vehicular edge computing, network performance and computing resources dynamically change, and vehicles should find the optimal strategy for offloading their tasks to servers to achieve a rapid computing service. In this paper, we address the multi-layered vehicle edge-computing framework, where each vehicle can choose one of three strategies for task offloading. For the best offloading performance, we propose a prediction-based task-offloading scheme for the vehicles, in which a deep-learning model is designed to predict the task-offloading result (success/failure) and service delay, and then the predicted strategy with successful task offloading and minimum service delay is chosen as the final offloading strategy. In the proposed model, an automatic feature-generation model based on CNN is proposed to capture the intersection of features to generate new features, avoiding the performance instability caused by manually designed features. The simulation results demonstrate that each part of the proposed model has an important impact on the prediction accuracy, and the proposed scheme has the higher Area Under Curve (AUC) than other methods. Compared with SVM- and MLP-based methods, the proposed scheme has the average failure rate decreased by 21.2 % and 6.3 % , respectively. It can be seen that our prediction-based scheme can effectively deal with dynamic changes in network performance and computing resources.

Keywords: vehicular edge computing; task offloading; performance prediction; deep learning; service delay (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/7/1010/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/7/1010/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:7:p:1010-:d:776467

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().