An Approach to Robust Resource Allocation in Large-Scale Systems of Systems

Resource allocation, in terms of balancing supply and demand, is a common problem in many mission-critical systems, such as smart grids. If the participants' contribution is subject to inertia, they have to solve the problem for a specific time span in advance, that is, create schedules on the basis of demand predictions. To improve the system's stability, the participants not only have to anticipate the predictions' inherent uncertainties, but also provide an appropriate amount of degrees of freedom, i.e., Reserves, that enable reactive contribution adjustments. Reserves are of particular interest because the problem's complexity and uncertainty call for rather coarse grained schedules and an anticipation can turn out to be wrong. In this paper, we present an approach to robust resource allocation in self-organizing hierarchical systems that is flanked by an auction-based algorithm. To deal with uncertain demand, agents learn characteristic prediction errors and create schedules for different possible developments of the demand. This allows the agents to choose the most suitable schedule at runtime. Further, they schedule feedback-driven reserves to be able to cope with unforeseen situations. Based on the example of creating power plant schedules in decentralized autonomous power management systems, we show that our auction-based algorithm clearly outperforms a regio-central approach.