Stochastic traffic engineering for demand uncertainty and risk-aware network revenue management

Abstract

Stochastic traffic engineering for demand uncertainty and risk-aware network revenue management We present a stochastic traffic engineering framework for optimizing bandwidth provisioning and route selection in networks. Traffic demands are uncertain and specified by probability distributions, and the objective is to maximize a risk-adjusted measure of network revenue that is generated by serving demands. Considerable attention is given to the appropriate measure of risk in the network model. We also advance risk-mitigation strategies. The optimization model, which is based on mean-risk analysis, enables a service provider to maximize a combined measure of mean revenue and revenue risk. The framework is intended for off-line traffic engineering, which takes a centralized view of network topology, link capacity and demand. We obtain conditions under which the optimization problem is an instance of convex programming. We study the properties of the solution and show that it asymptotically meets the stochastic efficiency criterion.In our numerical investigations we illustrate the impact of demand uncertainty on various aspects of the optimally traffic engineered solutions. The service provider's tolerance to risk is shown to have a strong influence on the traffic engineering and revenue management decisions. We develop the efficient frontier, which is the set of Pareto optimal pairs of mean revenue and revenue risk, to aid the service provider in selecting its operating point.