We consider the downlink of an orthogonal frequency division multiplexing (OFDM) based cell that accommodates calls from different service-classes with different resource requirements. We assume that calls arrive in the cell according to a quasi-random process, i.e., calls are generated by a finite number of sources. To calculate the most important performance metrics in this OFDM-based cell, i.e., congestion probabilities and resource utilization, we model it as a multirate loss model, show that the steady-state probabilities have a product form solution (PFS) and propose recursive formulas which reduce the complexity of the calculations. In addition, we study the bandwidth reservation (BR) policy which can be used in order to reserve subcarriers in favor of calls with high subcarrier requirements. The existence of the BR policy destroys the PFS of the steady-state probabilities. However, it is shown that there are recursive formulas for the determination of the various performance measures. The accuracy of the proposed formulas is verified via simulation and found to be satisfactory.
In the article, a validation module, being a component of an integrated system supporting routing in software defined networks (SDNRoute), is proposed and thoroughly examined. The module allows for the verification of the results provided by the optimization module before these results are deployed in the production network. Routing policies are validated for their impact on the network quality parameters and against the threat of overloading (congestion).
Establishing the proper values of controller parameters is the most important thing to design in active queue management (AQM) for achieving excellent performance in handling network congestion. For example, the first well known AQM, the random early detection (RED) method, has a lack of proper parameter values to perform under most the network conditions. This paper applies a Nelder-Mead simplex method based on the integral of time-weighted absolute error (ITAE) for a proportional integral (PI) controller using active queue management (AQM). A TCP flow and PI AQM system were analyzed with a control theory approach. A numerical optimization algorithm based on the ITAE index was run with Matlab/Simulink tools to find the controller parameters with PI tuned by Hollot (PI) as initial parameter input. Compared with PI and PI tuned by Ustebay (PIU) via experimental simulation in Network Simulator Version 2 (NS2) in five scenario network conditions, our proposed method was more robust. It provided stable performance to handle congestion in a dynamic network.