Global Pricing in Large Scale Computational Markets

Abstract

Scale up the number of computing resources is a challenging issue when building a global computational system. For this purpose, we present an approach that adopts the commodity market model as an economic incentive model and ensures the balance between supply and demand. We show how this model may be adapted and applied to a large scale computational infrastructure. To achieve a competitive equilibrium, prices are adjusted according to a tâtonnement like process. However, this process, like several other pricing algorithms proposed in the literature, does not fulfill the scalability requirement: all prices of all commodities are computed by only one auctioneer. In the present work, a fully distributed pricing algorithm is proposed based on an existing partially distributed version. While in this last version, the price of each commodity is computed by only one auctioneer, in our algorithm, a variable number of auctioneers is used. To each auctioneer is associated a limited number of consumers and suppliers with low communication delay. Our algorithm is then scalable with respect to the number of suppliers and consumers. To evaluate our algorithm, we have performed a simulation study. For different number of auctioneers per commodity, the experimental results show that our algorithm converges as well as the partially distributed version. Moreover, by splitting the search space among auctioneers, our algorithm accelerates the convergence.