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In this paper, an optimization approach to flow control in data networks is proposed. The model objective is to maximize the aggregate utilities of the data sources over soft bounds and delay constraints. The network links and data sources are considered as processors of a distributed computation system with a global objective function. The presented model works with different shapes of utility curves under the proposition of elastic data traffic. The approach relies on the observed delay as a measure of the network congestion at the routers. A primal-dual algorithm carried out by the data sources is used to solve the optimization problem in a decentralized manner. The algorithm solves for the rates without the access to any global information. The calculated rates conform to queue proportional fairness. Later, we present a simple approach for estimating the minimum roundtrip delay followed by simulation experiments.