As an indispensable key technology in 5G Internet of Things (IoT), mobile edge computing (MEC) provides a variety of computing and services at the edge of the network for energy-limited and computation-constrained mobile devices (MDs). In this paper, we use the multiaccess characteristics of 5G heterogeneous networks and queuing theory. By considering the heterogeneity of base stations, we establish the waiting and transmission consumption model when tasks are offloaded. Then, the problem of jointly optimizing the energy and delay consumption of MDs is proposed. We adopt an optimization scheme based on task assignment probability; moreover, the task assignment algorithm based on quasi-Newton interior point (TA-QNIP) method is developed to solve the optimization issue. Compared with the Newton interior point algorithm, the proposed algorithm accelerates the convergence speed and reduces the complexity of the algorithm and is closer to the objective function optimal solution. The simulation results demonstrate that the proposed method can reduce the total consumption of MDs effectively; furthermore, the performance of the algorithm is proved.

中文翻译：

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基于排队论的5G异构网络移动边缘计算任务分配优化方案

作为5G物联网（IoT）必不可少的关键技术，移动边缘计算（MEC）在网络边缘为能量受限和计算受限的移动设备（MD）提供了各种计算和服务。在本文中，我们使用5G异构网络的多址访问特性和排队理论。通过考虑基站的异构性，我们建立了任务卸载时的等待和传输消耗模型。然后，提出了联合优化MD的能量和延迟消耗的问题。我们采用基于任务分配概率的优化方案；此外，开发了基于拟牛顿内点法（TA-QNIP）的任务分配算法来解决优化问题。与牛顿内点算法相比，该算法加快了收敛速度，降低了算法的复杂度，更接近目标函数的最优解。仿真结果表明，该方法可以有效降低MD的总消耗。进一步证明了该算法的性能。