End-to-End Distributed Flow Control for Networks with Nonconcave Utilities

This paper proposes near-optimal decentralized allocation for traffic generated by real-time applications in communication networks. The quality of experience perceived by users in practical applications cannot be accurately modeled using concave functions. Therefore, we tackle the problem of optimizing general nonconcave network utilities. The approach for solving the resulting nonconvex network utility maximization problem relies on designing a sequence of convex relaxations whose solutions converge to a point that characterizes an optimal solution of the original problem. Three different algorithms are designed for solving the proposed convex relaxation, and their theoretical convergence guarantees are studied. All proposed algorithms are distributed in nature, where each user independently controls its traffic in a way that drives the overall network traffic allocation to an optimal operating point subject to resource constraints. All computations required by the algorithms are performed independently and locally at each user using local information available to that user. We highlight the tradeoff between the convergence speed and the network overhead required by each algorithm. Furthermore, we demonstrate the robustness and scalability of these algorithms by showing that traffic is automatically rerouted in case of a link failure or having new users joining the network. Numerical results are presented to validate our findings.