基于云雾混合计算的车联网联合资源分配算法

针对车联网业务的低时延、低功耗需求及海量设备计算卸载引起的网络拥塞问题，该文提出一种在云雾混合网络架构下的联合计算卸载、计算资源和无线资源分配算法(JODRAA)。首先，该算法考虑将云计算与雾计算结合，以最大时延作为约束，建立最小化系统能耗和资源成本的资源优化模型。其次，将原问题转化为标准二次约束二次规划(QCQP)问题，并设计一种低复杂度的联合卸载决策和计算资源分配算法。进一步，针对海量设备计算卸载引起的网络拥塞问题，建立卸载用户接入请求队列的上溢概率估计模型，提出一种基于在线测量的雾节点时频资源配置算法。最后，借助分式规划理论和拉格朗日对偶分解方法得到迭代的带宽和功率分配策略。仿真结果表明，该文算法可以在满足时延需求的前提下，最小化系统能耗和资源成本。

Joint Resource Allocation Algorithms Based on Mixed Cloud/Fog Computing in Vehicular Network

For the problems of low delay, low power requirement and access congestion caused by computational unloading of mass devices, a Joint Offloading Decision and Resource Allocation Algorithm (JODRAA) is proposed based on cloud-fog hybrid network architecture. Firstly, the algorithm considers the combination of cloud and fog computing, and establishes a resource optimization model to minimize system energy consumption and resource cost with maximum delay as constraint. Secondly, the original problem is transformed into a standard Quadratically Constrained Quadratic Program (QCQP) problem, and a low-complexity joint unloading decision-making and computational resource allocation algorithm is designed. Furthermore, considering the access congestion problem caused by massive computing of unloading devices, an estimation model of the overflow probability of unloading user access request queue is established, and an on-line measurement based time-frequency resource allocation algorithm for fog nodes is proposed. Finally, the iterative bandwidth and power allocation strategy is obtained by using fractional programming theory and Lagrange dual decomposition method. The simulation results show that the proposed algorithm can minimize the system energy consumption and resource cost on the premise of time delay.