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Multi-period Distributed Delay-Sensitive Tasks Offloading in a Two-Layer Vehicular Fog Computing Architecture

Part of the Communications in Computer and Information Science book series (CCIS,volume 1265)


Vehicular fog computing (VFC) is a new paradigm to extend the fog computing to conventional vehicular networks. Nevertheless, it is challenging to process delay-sensitive task offloading in VFC due to high vehicular mobility, intermittent wireless connection and limited computation resource. In this paper, we first propose a distributed VFC architecture, which aggregates available resources (i.e., communication, computation and storage resources) of infrastructures and vehicles. By considering vehicular mobility, lifetimes of tasks and capabilities of fog nodes, we formulate a multi-period distributed task offloading (MPDTO) problem, which aims at maximizing the system service ratio by offloading tasks to the suitable fog nodes at suitable periods. Then, we prove that the MPDTO problem is NP-hard. Subsequently, an Iterative Distributed Algorithm Based on Dynamic Programming (IDA_DP) is proposed, by which each fog node selects the appropriate tasks based on dynamic programming algorithm and each client vehicle determines the target fog node for its tasks according to the response delay. Finally, we build the simulation model and give a comprehensive performance evaluation, which demonstrate that IDA_DP can obtain the approximate optimal solution with low computational cost.


  • Vehicular Fog Computing (VFC)
  • Distributed scheduling
  • Task offloading
  • Delay-sensitive

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  • DOI: 10.1007/978-981-15-7670-6_38
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This work was supported in part by the National Natural Science Foundation of China under Grant No. 61872049, and in part by the Fundamental Research Funds for the Central Universities under Project No. 2020CDCGJ004.

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Correspondence to Kai Liu .

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Zhou, Y., Liu, K., Xu, X., Liu, C., Feng, L., Chen, C. (2020). Multi-period Distributed Delay-Sensitive Tasks Offloading in a Two-Layer Vehicular Fog Computing Architecture. In: Zhang, H., Zhang, Z., Wu, Z., Hao, T. (eds) Neural Computing for Advanced Applications. NCAA 2020. Communications in Computer and Information Science, vol 1265. Springer, Singapore.

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