Online Resource Inference in Network Utility Maximization Problems

TL;DR

This paper introduces an active learning-based overlay rate allocation scheme for network utility maximization that efficiently infers available resources and optimizes user data rates, improving network performance under uncertain resource availability.

Contribution

It proposes a novel active learning approach integrated into NUM to estimate unknown resources and enhance rate allocation in complex network scenarios.

Findings

Active learning accelerates resource estimation.

The scheme improves long-term service quality.

Efficient rate allocation under resource uncertainty.

Abstract

The amount of transmitted data in computer networks is expected to grow considerably in the future, putting more and more pressure on the network infrastructures. In order to guarantee a good service, it then becomes fundamental to use the network resources efficiently. Network Utility Maximization (NUM) provides a framework to optimize the rate allocation 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 congestion measurements, a passive learning of the available resources would not lead to satisfying performance. The coordination scheme must then perform active learning in order to speed up the resources estimation and quickly increase the system performance. By adopting 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 quality of the service delivered to the users.