# Non-Concave Network Utility Maximization in Connectionless Networks: A Fully Distributed Traffic Allocation Algorithm

## TL;DR

This paper introduces a fully distributed algorithm for traffic allocation in connectionless networks with non-concave utility functions, using convex relaxation and hierarchy of problems to achieve near-optimal solutions.

## Contribution

It develops a novel hierarchy of problems and a distributed iterative algorithm to solve non-convex traffic optimization with convergence guarantees.

## Key findings

- Algorithm converges at a rate of O(1/K).
- Numerical simulations validate the effectiveness of the proposed method.
- Convex relaxation provides a practical approach to non-concave utility maximization.

## Abstract

This paper considers the optimization-based traffic allocation problem among multiple end points in connectionless networks. The network utility function is modeled as a non-concave function, since it is the best description of the quality of service perceived by users with inelastic applications, such as video and audio streaming. However, the resulting non-convex optimization problem, is challenging and requires new analysis and solution techniques. To overcome these challenges, we first propose a hierarchy of problems whose optimal value converges to the optimal value of the non-convex optimization problem as the number of moments tends to infinity. From this hierarchy of problems, we obtain a convex relaxation of the original non-convex optimization problem by considering truncated moment sequences. For solving the convex relaxation, we propose a fully distributed iterative algorithm, which enables each node to adjust its date allocation/ rate adaption among any given set of next hops solely based on information from the neighboring nodes. Moreover, the proposed traffic allocation algorithm converges to the optimal value of the convex relaxation at a $O(1/K)$ rate, where $K$ is the iteration counter, with a bounded optimality. At the end of this paper, we perform numerical simulations to demonstrate the soundness of the developed algorithm.