  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 demands (tasks with different priority values) with different latency
and data rate constraints. In this paper, our goal is to maximize the total
number of tasks served by a heterogeneous network, labeled task throughput, in
the presence of data rate and latency constraints and device preferences
regarding computational needs. Since our original problem is intractable, we
propose an efficient solution based on graph-coloring techniques. We
demonstrate the effectiveness of our proposed algorithm using numerical
results, real-world experiments on a laboratory testbed and comparing with the
state-of-the-art algorithm.
