Online Resource Inference in Network Utility Maximization Problems
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The amount of transmitted data in computer networks is expected to grow considerably in the next years, putting more and more pressure on the network infrastructures. In order to guarantee a good service, it becomes fundamental to use the network resources efficiently. Network Utility Maximization (NUM) provides a framework for distributed optimization of users' utilities when network resources are limited. Unfortunately, in the scenario where the amount of available resources is not known a priori classical NUM solving methods do not offer a viable solution. To overcome this limitation we design an overlay rate allocation scheme that attempts to infer the actual amount of available network resources while coordinating the users rate allocation. Due to the general and complex model assumed for the resource inference a passive learning of these resources would not lead to satisfying performance. The coordination scheme must perform active learning in order to speed up the resources estimation and quickly increase the delivered performances. By using an optimal learning formulation we are able to balance the tradeoff between an accurate estimation and an effective resources exploitation in order to maximize the long term performance delivered to the users.
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