Abstract
In this paper, we study the joint optimization of task allocation and charging scheduling of mobile charging vehicles (MCVs) for parked-vehicle-assisted edge computing networks. In the proposed model, a group of electric vehicles (EVs) that have been parked for a long time must be recharged to their expected energy level within a specified time frame. Meanwhile, an optimal set of parked vehicles (PVs) is selected to compute a machine learning task utilizing their hardware resources and local data while satisfying the task’s training performance requirements. Within the calculated time window, an MCV is dispatched to provide power replenishment to the PVs. By jointly deciding the task allocation and MCV charging sequence, the proposed model seeks to minimize the total energy consumption of the parked vehicular network, which includes the PV computation and MCV traveling consumption, subject to the PVs’ expected energy level, task target utility and time window. To address this joint optimization problem, a marginal-product-based algorithm is designed, where a deep reinforcement learning method is integrated to solve the MCV scheduling problem. Simulation results demonstrate that the proposed method can efficiently solve the problem and outperform the compared algorithms in terms of energy consumption.
This work is supported by the National Natural Science Foundation of China under grant No. 62171218.
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Zhang, W., Wang, R., Yi, C., Zhu, K. (2022). Joint Optimization of Computation Task Allocation and Mobile Charging Scheduling in Parked-Vehicle-Assisted Edge Computing Networks. In: Wang, L., Segal, M., Chen, J., Qiu, T. (eds) Wireless Algorithms, Systems, and Applications. WASA 2022. Lecture Notes in Computer Science, vol 13473. Springer, Cham. https://doi.org/10.1007/978-3-031-19211-1_34
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