In this paper we study the problem of large-scale resource congestion from the control and regulation point of view. Applications and services running in large-scale shared infrastructures like Grids or PlanetLab have di erent resource usage pro les and di erent resource consumption strategies according to their speci c requirements. However, users of these types of infrastructure tend to prefer a subset of available nodes to execute their tasks. As a result, this pattern of user behaviour usually leads to an unfair distribution of work between nodes -i.e some nodes are highly loaded while the others remain almost idle. We finnd that most current research focuses on short-term and per-resource scheduling, and the issue of efficient resource allocation in the long-term and system-wide is not yet appropriately studied. Thus, there is a need for controlling, distributing and limiting the capacity of each participant to consume resources considering the state of the system as a whole. Our main contribution is the introduction of a novel macro-scheduling (long-term and system-wide) mechanism for resource capacity self-regulation in which virtual currency or money is used as a tool to govern resource and service usage in massively distributed settings, which are otherwise hard to control. We show by simulation that our approach successfully redistributes the load in a fair and economically-efficient manner.