Parked vehicle edge computing (PVEC) utilizes both idle resources in parked vehicles (PVs) and roadside units (RSUs) as service providers (SPs) to improve the performance of vehicular internet of things (IoT). However, it is difficult to make optimal service migration decisions in PVEC networks due to the uncertain parking duration and resources heterogeneity of PVs. In this paper, we formulate the service migration of all the vehicles as an optimization problem with the objective of minimizing the average latency. We propose a two-stage service migration algorithm for PVEC networks, which divides the original problem into the service migration between SPs and the serving PV selection in parking lots. The service migration between SPs is transformed to an online problem based on Lyapunov optimization, where the expected parking duration of PVs is utilized. A modified Hungarian algorithm is proposed to select the PVs for migration. A series of simulation experiments based on the real-world vehicle traces are conducted to verify the superior performance of the proposed two-stage service migration (SEA) algorithm as compared with the state-of- art solutions.
Vehicular Fog Computing (VFC) is a promising technique to enable ultra low service latency by exploiting the computation and storage resources of both Roadside Units (RSUs) and Serving Vehicles (SVs) such as buses and trams with rich resources. To tackle with the mobility of vehicles, the services are usually migrated between RSUs and SVs, i.e., follow the vehicle, to maintain the benefits of VFC. However, making optimal service migration decisions in VFC is challenging due to the mobility of SVs and the interference between vehicles. In this paper, we investigate multi-vehicle service migration problem in VFC. We propose an efficient online algorithm, called FEE, to optimize the service migration for each vehicle in each time slot, where the latency in the current time slot, the expected latency in future time slots, and the interference among vehicles are minimized. The expected latency in future times slots is obtained by trajectory prediction based on hidden Markov model, and the interference is measured based on the server load. Finally, a series of simulations based on real-world mobility traces of Rome taxis are conducted to verify the superior performance of the proposed FEE algorithm as compared with the state-of-the-art solutions.
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