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RtDS: real-time distributed strategy for multi-period task offloading in vehicular edge computing environment


With recent advances in sensing technologies and the emerging intelligent transportation system applications, smart vehicles impose huge requirements on processing computation-intensive tasks with strict time constraints, which cannot be satisfied solely relying on local computation resources. Vehicular edge computing is an efficient paradigm for enabling low-latency and high-quality service. In this paper, we consider a multi-period task offloading scenario in vehicular edge computing environment, where tasks can be offloaded in any period during their lifetime. Then, we formulate the multi-period offloading problem (MOP) to maximize the task completion ratio, by analyzing the mobility-aware communication model, resources-aware computation model and deadline-aware award model. Further, considering the high mobility of vehicles and dynamic wireless environments, we propose a real-time distributed strategy (RtDS) to solve MOP by exploiting the collaboration among edge nodes and client vehicles. Finally, we build the simulation model based on real vehicular trajectories and give a comprehensive performance evaluation, which demonstrates the superior performance of RtDS.

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This work was supported in part by the National Natural Science Foundation of China under Grant No.61872049, No.61802263 and No.62072064.

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Correspondence to Kai Liu.

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Liu, C., Liu, K., Ren, H. et al. RtDS: real-time distributed strategy for multi-period task offloading in vehicular edge computing environment. Neural Comput & Applic (2021).

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  • Vehicular edge computing
  • Multi-period task offloading
  • Real-time
  • Distributed strategy