Abstract
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.
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