Using economic regulation to prevent resource congestion in large-scale shared infrastructures

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 different resource usage profiles and different resource consumption strategies according to their specific 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 find 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.