Enhancing the User Experience in Vehicular Edge Computing Networks: An Adaptive Resource Allocation Approach
Xiaoke Sun, Junhui Zhao, Xiaoting Ma, Qiuping Li · IEEE Access · 2019
Mobile edge computing (MEC) has been developed as a key technique to handle the explosive computation demands of vehicles. However, it is non-trivial to realize high-reliable and low-latency vehicular requirements among distributed and capacity-constrained MEC nodes. Besides, the dynamic and uncertain vehicular environments bring extra challenges to preserve the long-term satisfactory user experience. In this paper, an adaptive resource allocation approach is investigated to enhance the user experience in vehicular edge computing networks. Specifically, leveraging the idea of task scalability, a model for balancing computing quality and resource consumption is introduced to exploit the computational resources fully. Towards the goal of minimizing the long-term computing quality loss by specifying the needed resource and the expected quality of each running task, a mix-integer non-linear stochastic optimization problem is formulated to jointly optimize the allocation of radio and computing resources, as well as the task placement. Due to the unpredictable network states and the high computational complexity of the formulated problem, the long-term optimization problem is firstly decomposed into a series of one-slot problems, and then, an iterative algorithm is provided to derive a computation efficient solution. Finally, both rigorous theoretical analysis and extensive trace-driven simulations validate the efficacy of our proposed approach.