Reliability-Aware Resource Allocation for Vehicular Services in Mobile Edge Computing

Le résumé fourni par la source

Mobile edge computing is envisioned as a promising paradigm to overcome the computational limitations of vehicles. However, current resource allocation optimizations for vehicular services in mobile edge computing are typically performed under quasi-static scenarios. Due to the time-varying wireless communication environments between vehicles and the base station, the communication uncertainty becomes a key factor to hinder the service reliability. To tackle this problem, the reliability-aware mobile edge resource allocation for vehicular services is explored in this paper. Firstly, the model of computation, communication, and reliability is reviewed integratively, and the problem is then formulated as a fractional programming model. Secondly, conditional value-at-risk (CVaR) is adopted to transform the original problem into a solvable semidefinite programming model. Finally, we propose an efficient two-layer reliability-aware resource allocation approach that decomposes the original problem into two sub-problems with lower complexity. Lagrange multiplier and Dinkelbach strategy are then applied for these sub-problems, which minimize average energy consumption while ensuring reliability for vehicles. Simulation results examine the performance of our proposed approach and demonstrate its superiority over other approaches.

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