Traffic Engineering with Elastic Traffic

In the network layer, an internet service provider controls the traffic across an autonomous system by load balancing via traffic engineering and by varying the offered traffic of the users via feedback signals. In the transport layer, users send traffic into the network using the TCP protocol, which adjusts offered traffic according to the received feedback. We investigate how feedback and current traffic engineering practice interact with congestion control under the network utility maximization framework. We show that the current interaction is stable, increases network utility, but does not necessarily improve the traffic engineering objective. The mismatch in outcome and incentives prompt us for a more holistic view using game theory. With suitable modification of the feedback, we show that the interaction converges to a socially optimal solution for users running either primal or dual algorithms. We further show that the results hold even when traffic engineering is performed at any irregular intervals. More generally, we show via heterogeneous feedback the same optimality result for a mix of users running primal and dual algorithms.