An Explorative Model for Federated Trading in Distributed Computing Environments

We propose a model for trading in very large-scale distributed computing environments which is based on the gradual evolution of a federated trading space through a process of continual exploration and evaluation, rather than on the imposition of a strictly managed hierarchy. In our model, each trader autonomously acquires local knowledge of the trading space, called trading knowledge, through a process of distributed resource discovery. Trading knowledge typically consists of trader links which reference other traders. Trader links also contain a measure of affinity: a strength of attraction based on a comparison of so called service and interest profiles, perhaps combined with a history of how useful and reliable other traders have proved to be. This notion of affinity helps a trader to decide how to resolve import requests which cannot be satisfied locally. It also helps a trader to decide which trader links to retain as it periodically and autonomously explores the trading environment. Instead of being concerned with the detailed management of individual trader links, human managers can then control the evolution of the trading space through a number of high level management policies. These include service and interest profiles, definitions of affinity and instructions on when and how exploration should occur. Our paper also describes a reference implementation of the model within the ANSAware distributed processing environment called the Explorative Trading Service (ETS).