Online Pricing for Bandwidth Provisioning in Multi-class Networks

We consider the problem of pricing for bandwidth provisioning over a single link, where users arrive according to a known stochastic tra#c model. The network administrator controls the resource allocation by setting a price at every epoch, and each user's response to the price is governed by a demand function. We formulate this problem as a partially observable Markov decision process (POMDP), and explore two novel pricing schemes---reactive pricing and spot pricing---and compare their performance to appropriately tuned flat pricing. We use a gradient-ascent approach in all the three pricing schemes. We provide methods for computing the unbiased estimates of the gradient in an online (incremental) fashion. Our simulation results show that our novel schemes take advantage of the known underlying tra#c model and significantly outperform the model-free pricing scheme of flat pricing