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.