Stochastic approximation and transaction-level model for IP network design

We investigate the use of simulation and transaction level models for transmission control protocol (TCP) in Internet Protocol (IP) network design. More specifically, we focus on the transaction level dynamics of TCP and approximate it by max-min fair sharing. Based on this model, we formulate a network dimensioning problem as a nonlinear constrained optimization problem. The constraints and their gradients, which do not have analytical forms, are estimated through fluid simulation of the transaction-level model of TCP. The problem is solved by a gradient descent type of algorithm, with additional heuristics based techniques to improve its convergence. The performance of the proposed algorithm is evaluated through experimental studies on example networks. Results show that the methods are promising and can help the design of networks.