Flow control in high speed networks requires distributed routers to make fast decisions based only on local information in allocating bandwidth to connections.

While most previous work on this problem focuses on achieving local objective functions, in many cases it may be necessary to achieve global objectives such as maximizing the total flow, or benefit. This problem illustrates one of the basic aspects of distributed computing: achieving global objectives using local information. While this goal is often impossible to achieve, we show that it is possible to obtain an efficient, distributed approximation algorithm for optimizing global throughput in the context of the flow control problem.