Fog computing in 5G networks has played a significant role in increasing the number of users in a given network. However, Internet-of-Things (IoT) has driven system designers towards designing heterogeneous networks to support diverse task demands (e.g. heterogeneous tasks with different priority values) under interference constraints in the presence of limited communication and computational resources. In this paper, our goal is to maximize the total number of tasks served by an IoT-enabled 5G network, labeled task throughput, in the presence of heterogeneous task demands and limited resources. Since our original problem is intractable, we propose an efficient two-stage solution based on multi-graph-coloring. We analyze the computational complexity of our proposed algorithm, and prove the correctness of our algorithm. Lastly, simulation results are presented to demonstrate the effectiveness of the proposed algorithm, in comparison with state-of-the-art approaches in the literature.

5G网络中的雾计算在增加给定网络中的用户数量方面发挥了重要作用。但是，物联网（IoT）驱使系统设计人员着手设计异构网络，以在通信和计算资源有限的情况下，在干扰约束下支持各种任务需求（例如，具有不同优先级值的异构任务）。在本文中，我们的目标是最大化具有IoT功能的5G网络所服务的任务总数，并标记为任务吞吐量，在任务需求异构且资源有限的情况下。由于我们最初的问题很棘手，因此我们提出了一种基于多图着色的高效两阶段解决方案。我们分析了所提出算法的计算复杂度，并证明了该算法的正确性。最后，与文献中的最新方法相比，仿真结果表明了所提算法的有效性。