Conic Programming Approach to Reduce Congestion Ratio in Communications Network

Abstract

These researches introduce a robust optimization model to reduce the congestion ratio in communications network considering uncertainty in the traffic demands. The propose formulation is depended on a model called the pipe model. Network traffic demand is fixed in the pipe model and most of the previous researches consider traffic fluctuation locally. Our proposed model can deal with fluctuation in the traffic demands and considers this fluctuation all over the network. We formulate the robust optimization model in the form of second-order cone programming (SOCP) problem which is tractable by optimization software. The numerical experiments determine the efficiency of our model in terms of reducing the congestion ratio compared to the others model.