Analysis of bandwidth allocation on end-to-end QoS networks under budget control

AbstractThis paper considers the problem of bandwidth allocation on communication networks with multiple classes of traffic, where bandwidth is determined under the budget constraint. Due to the limited budget, there is a risk that the network service providers can not assert a 100% guaranteed availability for the stochastic traffic demand at all times. We derive the blocking probabilities of connections as a function of bandwidth, traffic demand and the available number of virtual paths based on the Erlang loss formula for all service classes. A revenue/profit function is studied through the monotonicity and convexity of the blocking probability and expected path occupancy. We present the optimality conditions and develop a solution algorithm for optimal bandwidth of revenue management schemes. The sensitivity analysis and three economic elasticity notions are also proposed to investigate the marginal revenue for a given traffic class by changing bandwidth, traffic demand and the number of virtual paths, respectively. By analysis of those monotone and convex properties, it significantly facilitates the operational process in the efficient design and provision of a core network under the budget constraint

Sensitivity of optimal prices to system parameters in a steady-state service facility

We consider the problem of maximizing the long-run average reward in a service facility with dynamic pricing. We investigate sensitivity of optimal pricing policies to the parameters of the service facility which is modelled as an M/M/s/K queueing system. Arrival process to the facility is Poisson with arrival rate a decreasing function of the price currently being charged by the facility. We prove structural results on the optimal pricing policies when the parameters in the facility change. Namely, we show that optimal prices decrease when the capacity of the facility or the number of servers in the facility increase. Under a reasonable assumption, we also show that optimal prices increase as the overall demand for the service provided by the facility increases or when the service rate of the facility decreases. We illustrate how these structural results simplify the required computational effort while finding the optimal policy.Queueing Stochastic programming Pricing Markov decision processes Robustness and sensitivity analysis