AN EXPLORATIVE APPROACH TO FEDERATED TRADING

We develop a new model for trading in large-scale distributed computin g environments which is based on the gradual evolution of a federated trading space through a process of continual exploration and evaluatio n, rather than on the imposition of a strictly managed structure. In o ur model, each trader autonomously acquires local knowledge of the tra ding space, called trading knowledge, through a process of distributed resource discovery. Trading knowledge typically consists of trader li nks which reference other traders. Trader links also contain a measure of affinity: a strength of attraction based on a comparison of so-cal led service and interest profiles, perhaps combined with a history of how useful and reliable other traders have proved to be. This notion o f affinity helps a trader to decide how to resolve import requests whi ch cannot be satisfied locally. It also helps a trader to decide which trader links to retain as it periodically and autonomously explores t he trading environment. Instead of being concerned with the detailed m anagement of individual trader links, human managers can then control the evolution of the trading space through a number of high-level mana gement policies. These include service and interest profiles, definiti ons of affinity and instructions on when and how exploration should oc cur. Our paper also describes a reference implementation of the model with the ANSAware distributed processing environment called the Explor ative Trading Service (ETS) which provides an exploration engine as we ll as various management interfaces (including a Gopher gateway so tha t various network information retrieval browsers may be used to browse the trading space). (C) 1998 Published by Elsevier Science B.V.