Optimization and sensitivity analysis of computer simulation models by the score function method

Experimental Design;Simulation;Optimization;Queueing Theory

Dynamic bandwidth allocation using infinitesimal perturbation analysis

Advances in network management and switching technologies make dynamic bandwidth allocation of logical networks built on top of a physical network possible. Previous proposed dynamic bandwidth allocation algorithms are based on simplified network model. The analytical model is valid only under restrictive assumptions. Infinitesimal Perturbation Analysis, a technique which estimates the gradients of the functions in discrete event dynamic systems by passively observing the system, is used to estimate delay sensitivities under general traffic patterns. A new dynamic bandwidth allocation algorithm using on-line sensitivity estimation is proposed. Simulation results show that the approach further improves network performance. Implementation of the proposed algorithm in operational networks is also discussed.published_or_final_versio

Exponential penalty function control of loss networks

We introduce penalty-function-based admission control policies to approximately maximize the expected reward rate in a loss network. These control policies are easy to implement and perform well both in the transient period as well as in steady state. A major advantage of the penalty approach is that it avoids solving the associated dynamic program. However, a disadvantage of this approach is that it requires the capacity requested by individual requests to be sufficiently small compared to total available capacity. We first solve a related deterministic linear program (LP) and then translate an optimal solution of the LP into an admission control policy for the loss network via an exponential penalty function. We show that the penalty policy is a target-tracking policy--it performs well because the optimal solution of the LP is a good target. We demonstrate that the penalty approach can be extended to track arbitrarily defined target sets. Results from preliminary simulation studies are included.Comment: Published at http://dx.doi.org/10.1214/105051604000000936 in the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org